Data report overview
The dataset examined has the following dimensions:
| Number of observations |
454 |
| Number of variables |
929 |
Variable list
id_date_creation
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
210 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-07", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-13", "2017-04-19", "2017-04-20", "2017-04-22", "2017-04-25", "2017-04-26", "2017-04-27", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-13", "2017-05-15", "2017-05-16", "2017-05-17", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-22", "2017-06-01", "2017-06-02", "2017-06-05", "2017-06-13", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-22", "2017-06-23", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-23", "2017-07-24", "2017-07-25", "2017-07-27", "2017-08-01", "2017-08-03", "2017-08-08", "2017-08-10", "2017-08-17", "2017-08-20", "2017-08-30", "2017-08-31", "2017-09-02", "2017-09-06", "2017-09-07", "2017-09-11", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-05", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-09", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-25", "2017-11-26", "2017-11-27", "2017-11-28", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-21", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-19", "2018-01-23", "2018-01-27", "2018-01-30", "2018-01-31", "2018-02-01", "2018-02-03", "2018-02-05", "2018-02-08", "2018-02-11", "2018-02-12", "2018-02-13", "2018-02-14", "2018-02-16", "2018-02-20", "2018-02-25", "2018-02-26", "2018-03-02", "2018-03-03", "2018-03-09", "2018-03-11", "2018-03-12", "2018-03-28", "2018-04-03", "2018-04-16", "2018-04-29", "2018-05-03", "2018-05-04", "2018-05-13", "2018-05-16", "2018-05-31", "2018-06-26", "2018-07-26", "2018-07-30", "2018-08-09", "2018-09-04", "2018-09-06", "2018-09-11", "2018-10-23", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-26", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-01", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-05", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-20", "2018-12-21", "2018-12-23", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-07", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-15", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-21", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-03", "2019-02-14", "2019-02-15", "2019-02-17", "2019-02-18", "2019-02-26", "2019-02-27", "2019-03-03", "2019-03-11", "2019-03-19", "2019-04-29".
id_anonymat
- The variable is a key (distinct values for each observation).
id_centre1
| Variable type |
integer |
| Number of missing obs. |
37 (8.15 %) |
| Number of unique values |
46 |
| Median |
221 |
| 1st and 3rd quartiles |
202; 251 |
| Min. and max. |
176; 299 |
id_centre2
| Variable type |
integer |
| Number of missing obs. |
372 (81.94 %) |
| Number of unique values |
31 |
| Median |
216 |
| 1st and 3rd quartiles |
202; 253 |
| Min. and max. |
178; 299 |
id_centre3
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
8 |
| Median |
220 |
| 1st and 3rd quartiles |
205.75; 243.25 |
| Min. and max. |
189; 305 |
id_date_nais
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
446 |
| Mode |
“1976-10-15” |
- Observed factor levels: "1944-01-12", "1944-04-10", "1950-08-03", "1953-05-07", "1955-01-07", "1958-09-14", "1959-04-16", "1959-06-23", "1960-04-30", "1960-06-24", "1961-07-28", "1963-04-18", "1963-05-09", "1963-10-26", "1964-06-21", "1965-04-03", "1965-07-26", "1966-02-02", "1966-08-03", "1966-08-28", "1967-09-04", "1967-12-10", "1968-01-27", "1968-05-20", "1968-10-11", "1968-10-17", "1968-10-21", "1968-12-07", "1969-03-19", "1969-07-21", "1969-10-08", "1969-11-10", "1969-11-13", "1970-03-25", "1970-07-07", "1970-08-20", "1970-09-04", "1971-03-06", "1971-09-13", "1971-12-14", "1972-03-21", "1972-05-05", "1972-06-24", "1972-08-21", "1973-04-27", "1973-05-02", "1973-06-01", "1973-07-15", "1973-11-20", "1974-02-02", "1974-04-04", "1974-06-24", "1974-07-16", "1974-07-21", "1974-11-21", "1974-11-24", "1974-12-13", "1975-04-02", "1975-07-18", "1975-07-19", "1975-07-31", "1975-09-24", "1975-11-19", "1975-12-07", "1975-12-23", "1976-03-12", "1976-04-28", "1976-06-28", "1976-06-30", "1976-07-27", "1976-09-13", "1976-09-30", "1976-10-15", "1976-12-28", "1977-03-24", "1977-04-08", "1977-04-09", "1977-04-23", "1977-05-01", "1977-05-21", "1977-05-31", "1977-06-09", "1977-07-25", "1977-08-26", "1977-09-17", "1977-10-01", "1977-10-07", "1977-10-29", "1977-12-20", "1978-02-25", "1978-04-06", "1978-04-15", "1978-05-20", "1978-05-21", "1978-10-04", "1978-12-19", "1979-03-01", "1979-04-30", "1979-05-05", "1979-06-08", "1979-06-10", "1979-07-07", "1979-07-10", "1979-07-14", "1979-07-22", "1979-08-07", "1979-08-17", "1979-08-29", "1979-09-12", "1979-09-25", "1979-11-01", "1980-01-27", "1980-04-13", "1980-05-16", "1980-05-24", "1980-07-09", "1980-09-18", "1980-12-06", "1980-12-18", "1981-01-04", "1981-01-23", "1981-01-28", "1981-03-23", "1981-04-25", "1981-05-08", "1981-05-15", "1981-07-07", "1981-07-11", "1981-08-03", "1981-08-07", "1981-08-08", "1981-08-11", "1981-08-24", "1981-10-06", "1981-11-13", "1981-11-17", "1981-11-20", "1981-11-25", "1981-12-11", "1982-02-24", "1982-03-30", "1982-05-05", "1982-05-07", "1982-05-26", "1982-06-14", "1982-10-27", "1982-11-01", "1982-11-06", "1982-11-17", "1982-11-24", "1982-12-01", "1983-01-22", "1983-01-30", "1983-02-11", "1983-03-03", "1983-03-14", "1983-03-21", "1983-03-28", "1983-05-04", "1983-05-05", "1983-05-26", "1983-06-05", "1983-08-10", "1983-08-15", "1983-09-21", "1983-10-11", "1983-11-28", "1984-01-10", "1984-03-05", "1984-04-01", "1984-04-22", "1984-05-01", "1984-05-23", "1984-05-25", "1984-06-14", "1984-06-22", "1984-07-18", "1984-09-04", "1984-09-18", "1984-09-21", "1984-12-28", "1985-03-29", "1985-05-15", "1985-05-22", "1985-06-13", "1985-06-20", "1985-06-21", "1985-07-09", "1985-07-20", "1985-08-03", "1985-08-05", "1985-08-11", "1985-08-12", "1985-08-23", "1985-10-28", "1985-11-05", "1985-12-04", "1986-02-17", "1986-02-24", "1986-04-16", "1986-05-11", "1986-05-13", "1986-06-17", "1986-06-19", "1986-06-21", "1986-07-09", "1986-07-18", "1986-10-03", "1986-10-24", "1986-11-07", "1986-11-28", "1986-12-30", "1987-02-09", "1987-02-27", "1987-03-03", "1987-03-06", "1987-03-10", "1987-04-12", "1987-05-15", "1987-05-16", "1987-05-27", "1987-06-01", "1987-07-06", "1987-07-11", "1987-08-20", "1987-08-21", "1987-10-05", "1987-11-03", "1987-11-13", "1987-11-19", "1988-01-15", "1988-01-19", "1988-01-22", "1988-02-14", "1988-03-12", "1988-03-27", "1988-04-29", "1988-05-12", "1988-05-18", "1988-05-28", "1988-06-12", "1988-06-27", "1988-07-22", "1988-07-26", "1988-08-11", "1988-08-14", "1988-08-24", "1988-08-30", "1988-09-06", "1988-09-10", "1988-10-20", "1988-11-15", "1988-12-21", "1989-01-14", "1989-01-21", "1989-03-20", "1989-03-23", "1989-04-03", "1989-05-07", "1989-06-15", "1989-07-01", "1989-07-07", "1989-07-26", "1989-08-03", "1989-08-09", "1989-08-23", "1989-09-09", "1989-09-19", "1989-09-20", "1989-11-02", "1989-11-16", "1989-12-27", "1989-12-29", "1990-01-15", "1990-01-30", "1990-02-08", "1990-02-26", "1990-04-06", "1990-04-07", "1990-04-09", "1990-05-07", "1990-06-03", "1990-06-06", "1990-06-08", "1990-06-12", "1990-06-13", "1990-07-04", "1990-08-07", "1990-08-16", "1990-08-18", "1990-08-27", "1990-11-29", "1990-12-28", "1991-01-11", "1991-01-26", "1991-01-27", "1991-02-10", "1991-02-14", "1991-03-15", "1991-03-24", "1991-04-20", "1991-06-16", "1991-06-24", "1991-07-03", "1991-08-24", "1991-08-30", "1991-11-04", "1991-12-01", "1991-12-02", "1991-12-07", "1991-12-09", "1991-12-12", "1991-12-17", "1992-01-04", "1992-01-24", "1992-03-02", "1992-03-27", "1992-04-12", "1992-04-25", "1992-06-16", "1992-07-02", "1992-07-06", "1992-08-03", "1992-10-01", "1992-12-08", "1993-01-17", "1993-01-21", "1993-03-05", "1993-04-07", "1993-04-27", "1993-06-01", "1993-07-08", "1993-07-17", "1993-08-26", "1993-09-02", "1993-09-03", "1993-09-30", "1993-10-06", "1993-10-15", "1993-10-25", "1993-10-28", "1993-11-01", "1993-11-18", "1993-12-08", "1994-01-15", "1994-02-26", "1994-06-08", "1994-06-15", "1994-07-02", "1994-07-05", "1994-07-07", "1994-07-21", "1994-09-23", "1994-10-22", "1994-12-14", "1995-01-24", "1995-02-01", "1995-02-20", "1995-02-21", "1995-03-06", "1995-03-08", "1995-03-10", "1995-06-28", "1995-07-28", "1995-08-21", "1995-09-22", "1995-09-23", "1996-02-09", "1996-02-15", "1996-05-03", "1996-06-03", "1996-06-18", "1996-08-08", "1996-09-10", "1996-10-09", "1996-10-26", "1996-11-05", "1996-12-08", "1997-01-01", "1997-01-10", "1997-02-25", "1997-03-26", "1997-04-18", "1997-04-29", "1997-04-30", "1997-05-20", "1997-05-26", "1997-07-29", "1997-08-18", "1997-08-31", "1997-09-02", "1997-09-11", "1997-09-27", "1997-10-24", "1997-12-14", "1997-12-27", "1998-04-01", "1998-04-06", "1998-04-15", "1998-04-20", "1998-06-03", "1998-10-21", "1998-12-17", "1999-01-27", "1999-02-26", "1999-05-21", "1999-05-25", "1999-05-28", "1999-06-25", "1999-07-03", "1999-07-06", "1999-07-23", "1999-07-31", "1999-08-25", "1999-09-16", "1999-10-28", "1999-11-18", "2000-02-17", "2000-04-19", "2000-04-26", "2000-05-03", "2000-07-12", "2000-10-26", "2001-05-07", "2001-06-25", "2001-09-06", "2001-10-05", "2001-10-07", "2001-11-04", "2001-11-08", "2001-12-29", "2002-01-30", "2002-03-07", "2002-05-30", "2002-08-26", "2002-09-21", "2002-11-05", "2002-12-06", "2003-02-24", "2003-06-27", "2003-10-20", "2004-01-10", "2004-01-19", "2004-03-14", "2004-05-25", "2004-11-20".
id_dep_nais
| Variable type |
integer |
| Number of missing obs. |
64 (14.1 %) |
| Number of unique values |
77 |
| Median |
59 |
| 1st and 3rd quartiles |
37; 74 |
| Min. and max. |
1; 974 |
id_lieu_nais
| Variable type |
integer |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
id_nom
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
297 |
| Mode |
“CHA” |
- Observed factor levels: "AGN", "AGR", "AHA", "ALB", "ALC", "ALG", "ALL", "AMI", "AMO", "AMZ", "AND", "ARN", "ARR", "ASA", "AUG", "BAC", "BAR", "BAT", "BAU", "BEA", "BEC", "BEL", "BEN", "BER", "BIG", "BIL", "BIN", "BLA", "BOI", "BON", "Bos", "BOS", "BOU", "BRA", "BRE", "BRI", "BRO", "BRU", "BUH", "BUO", "CAD", "CAL", "CAS", "CAT", "CAU", "CAZ", "CEL", "CER", "CHA", "CHE", "CHO", "CLE", "COD", "COE", "COL", "CON", "Cou", "COU", "CRE", "CUP", "CUY", "CZE", "CZI", "DAA", "DAL", "DAM", "DAR", "DAS", "DAU", "DEB", "DEC", "DEL", "DEM", "DEO", "DES", "DIE", "DIS", "DOR", "DOU", "DRU", "DUB", "DUC", "DUF", "DUG", "DUP", "DUR", "DUT", "DUV", "EGO", "ELI", "EME", "ENJ", "EST", "FAL", "FAT", "FAU", "FAV", "FER", "FIA", "FLA", "FLE", "FOL", "FON", "FOR", "FOU", "FUL", "GAI", "GAL", "GAR", "GAV", "GED", "GEL", "GEN", "GER", "GHE", "GIB", "GIC", "GIL", "GIR", "GLO", "GOI", "GOU", "GRA", "GRO", "GUE", "GUI", "GUR", "HAY", "HEM", "HER", "HID", "HOU", "HUY", "JAC", "JAN", "JEA", "JEN", "JOC", "JOS", "JOU", "JUH", "KER", "KOP", "KUP", "LAB", "LAC", "LAD", "LAF", "LAL", "LAM", "LAN", "LAP", "LAU", "LAV", "LEB", "LEC", "LEF", "LEG", "LEH", "LEM", "LEN", "LEO", "LEP", "LEQ", "LER", "LET", "LEV", "LIO", "LOE", "LOR", "LOT", "LOU", "LOY", "LUC", "LUM", "MAA", "MAE", "MAG", "MAI", "MAL", "MAN", "MAR", "MAS", "MAU", "MET", "MIC", "MIK", "MIL", "MIR", "MLY", "MOL", "MON", "MOR", "MOS", "MUC", "MUH", "NAM", "NEH", "NER", "NGU", "NIC", "NIO", "NOE", "NOI", "ORY", "OST", "OUL", "PAL", "PAP", "PAR", "PAT", "PAY", "PER", "PET", "PIE", "PIG", "PIL", "PIT", "POI", "POL", "PON", "POR", "POU", "POY", "PRE", "PRI", "QUE", "RAD", "RAF", "RAM", "RAV", "RAY", "REB", "RED", "REG", "REM", "REN", "RER", "REZ", "RIB", "RIC", "RIE", "RIP", "RIT", "ROB", "ROC", "ROL", "ROS", "ROU", "RUB", "RUF", "RUY", "SAL", "SAN", "SAV", "SAY", "SCH", "SEB", "SEI", "SEK", "SEN", "SEV", "SIB", "SIL", "SIM", "SIS", "SOL", "SOU", "SOY", "STR", "TAI", "TAL", "TAM", "TES", "THE", "THI", "THO", "TIS", "TRI", "TRU", "USH", "VAL", "VAN", "VER", "VIA", "VIG", "VIO", "VIT", "VUL", "VUY", "WAL", "WEI", "WER", "WYO", "XAV", "YAK", "ZAH".
id_nom_jeune
| Variable type |
character |
| Number of missing obs. |
322 (70.93 %) |
| Number of unique values |
106 |
| Mode |
“LEF” |
- Observed factor levels: "ALL", "ANG", "ASS", "AV", "BAR", "BEL", "BER", "BEU", "BIL", "BRE", "BRI", "BRU", "CAL", "CHA", "CHE", "CIG", "CLA", "COI", "COL", "Cou", "CUY", "CZE", "DAL", "DAS", "DEB", "DEC", "DEL", "DES", "DEV", "DUB", "DUF", "DUM", "FAL", "FOR", "FOU", "FUL", "GAU", "GOI", "GUG", "GUI", "HAN", "HEM", "HOY", "IVA", "JEA", "JEF", "JOL", "JOU", "KER", "LAG", "LAU", "LEC", "LED", "LEF", "LEG", "LEO", "LEP", "LIC", "LOC", "LOI", "LOU", "MAH", "MAR", "MIL", "MOK", "MON", "MOR", "NEH", "ORL", "PAR", "PAU", "PER", "PIT", "PIZ", "PRI", "PRO", "QUE", "RAY", "RED", "REG", "RIE", "ROS", "ROU", "RUY", "SAG", "SAL", "SEI", "SIS", "SIT", "SOL", "STA", "TAU", "TAV", "TEJ", "THI", "THO", "TIS", "TOI", "TOU", "VAN", "VEN", "VIA", "VIL", "VIO", "VIS", "ZAH".
id_prenom
| Variable type |
character |
| Number of missing obs. |
6 (1.32 %) |
| Number of unique values |
98 |
| Mode |
“MA” |
- Observed factor levels: "AD", "AL", "AM", "AN", "AS", "AT", "AU", "BA", "BE", "CA", "Ce", "CE", "CH", "CL", "CO", "CY", "DA", "DE", "DI", "DO", "DR", "DY", "ED", "EL", "EM", "ER", "ES", "EV", "FA", "FL", "FR", "GE", "GH", "GR", "GU", "GW", "HE", "HU", "IN", "IS", "JC", "JE", "JI", "JO", "JP", "JU", "KA", "KE", "KI", "LA", "LE", "LI", "LO", "LU", "LÙ", "LY", "MA", "ME", "MI", "MO", "MU", "MY", "NI", "NO", "OD", "OL", "OP", "OU", "PA", "PE", "PH", "PI", "PL", "QU", "RA", "RE", "RÉ", "RO", "SA", "SE", "SO", "ST", "SU", "SY", "TE", "TÉ", "TH", "TI", "UL", "VA", "VE", "VI", "WI", "XA", "YA", "YO", "ZA", "ZO".
id_sexe
| Variable type |
integer |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
2 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
id_type
- The variable only takes one (non-missing) value: "P". The variable contains 0 % missing observations.
id_age
| Variable type |
integer |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
50 |
| Median |
30 |
| 1st and 3rd quartiles |
25; 38 |
| Min. and max. |
14; 74 |
id_tab_db
| Variable type |
integer |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
id_sep
| Variable type |
character |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
21 |
| Mode |
“BTWKW_WELSE” |
- Observed factor levels: "BTWKW_WELSE", "DMLIJ_HWVVT", "DNWZK_TEOOO", "DYTKD_ZHARX", "FEKTM_KLIFT", "GAYSH_XWGEQ", "GCXSD_VJHNY", "GXHPT_KRYPP", "HSOMH_GFPQP", "IBWZT_SJERR", "JNYQS_WYONK", "KAHVO_FRBQR", "LJHGZ_IMTKF", "MGDIU_DYHCM", "OXKAC_OFXOJ", "RJLOD_GRXQD", "RKWYE_DBRRR", "VMPCE_YDQEF", "WNGMA_QZSDI", "YASAA_WCNVO", "ZWFGU_XCTKR".
id_link
- The variable is a key (distinct values for each observation).
sc_date_creation
| Variable type |
character |
| Number of missing obs. |
51 (11.23 %) |
| Number of unique values |
192 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-07", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-13", "2017-04-19", "2017-04-20", "2017-04-22", "2017-04-25", "2017-04-26", "2017-04-27", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-16", "2017-05-17", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-24", "2017-06-01", "2017-06-02", "2017-06-13", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-22", "2017-06-23", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-24", "2017-07-25", "2017-07-27", "2017-08-01", "2017-08-03", "2017-08-08", "2017-08-10", "2017-08-17", "2017-08-20", "2017-08-30", "2017-09-06", "2017-09-07", "2017-09-11", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-05", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-09", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-28", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-21", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-23", "2018-01-27", "2018-01-30", "2018-01-31", "2018-02-01", "2018-02-04", "2018-02-08", "2018-02-11", "2018-02-12", "2018-02-14", "2018-02-16", "2018-02-20", "2018-02-25", "2018-02-26", "2018-03-09", "2018-03-11", "2018-03-12", "2018-03-28", "2018-04-16", "2018-04-29", "2018-05-03", "2018-05-04", "2018-05-13", "2018-05-16", "2018-05-31", "2018-06-26", "2018-07-26", "2018-07-30", "2018-08-09", "2018-09-04", "2018-09-11", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-26", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-05", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-20", "2018-12-21", "2018-12-23", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-07", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-15", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-21", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-03", "2019-02-14", "2019-02-17", "2019-02-18", "2019-02-26", "2019-02-27", "2019-03-03", "2019-03-12", "2019-04-29".
sc_an_diplome
| Variable type |
integer |
| Number of missing obs. |
89 (19.6 %) |
| Number of unique values |
39 |
| Median |
2010 |
| 1st and 3rd quartiles |
2002; 2015 |
| Min. and max. |
1961; 2018 |
sc_diplome
| Variable type |
integer |
| Number of missing obs. |
69 (15.2 %) |
| Number of unique values |
9 |
| Median |
6 |
| 1st and 3rd quartiles |
4; 8 |
| Min. and max. |
1; 999 |
sc_diplome_autre
| Variable type |
character |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
9 |
| Mode |
“Bac + 6” |
- Observed factor levels: "Bac + 6", "Bp préparateur en pharmacie", "CAPES", "CESS art de l’espace", "diplome auxiliaire puériculture", "Diplômé de niveau Bac +1 (BTS)", "Diplome niveau IV technicien électronique", "equivalent au brevet des collége", "Licence professionnel en Ressources Humaine".
sc_fin_etudes
| Variable type |
integer |
| Number of missing obs. |
64 (14.1 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
sc_plan
| Variable type |
integer |
| Number of missing obs. |
68 (14.98 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
sc_plan_an
| Variable type |
integer |
| Number of missing obs. |
428 (94.27 %) |
| Number of unique values |
15 |
| Median |
2003 |
| 1st and 3rd quartiles |
1999.5; 2005 |
| Min. and max. |
1988; 2011 |
sc_plan_demande
| Variable type |
integer |
| Number of missing obs. |
139 (30.62 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
sc_plan_ets1_1
- The variable only takes one (non-missing) value: "1". The variable contains 94.05 % missing observations.
sc_plan_ets1_2
- The variable only takes one (non-missing) value: "1". The variable contains 94.27 % missing observations.
sc_plan_ets1_3
- The variable only takes one (non-missing) value: "1". The variable contains 94.27 % missing observations.
sc_plan_ets1_4
- The variable only takes one (non-missing) value: "1". The variable contains 94.05 % missing observations.
sc_plan_ets1_5
- The variable only takes one (non-missing) value: "1". The variable contains 94.05 % missing observations.
sc_plan_ets1_6
- The variable only takes one (non-missing) value: "1". The variable contains 92.51 % missing observations.
sc_plan_ets1_7
- The variable only takes one (non-missing) value: "1". The variable contains 92.29 % missing observations.
sc_plan_ets1_8
- The variable only takes one (non-missing) value: "1". The variable contains 92.51 % missing observations.
sc_plan_ets1_9
- The variable only takes one (non-missing) value: "1". The variable contains 92.29 % missing observations.
sc_plan_ets1_12
- The variable only takes one (non-missing) value: "1". The variable contains 92.73 % missing observations.
sc_plan_ets1_13
- The variable only takes one (non-missing) value: "1". The variable contains 92.73 % missing observations.
sc_plan_ets1_14
- The variable only takes one (non-missing) value: "1". The variable contains 92.73 % missing observations.
sc_plan_ets2_1
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_plan_ets2_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
sc_plan_ets2_3
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
sc_plan_ets2_4
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
sc_plan_ets2_5
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_ets2_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_ets2_7
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_ets2_8
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_ets2_9
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_plan_ets2_14
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_plan_ets5_13
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_ets5_14
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_notification_1
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_plan_notification_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
sc_plan_notification_3
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_notification_4
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_plan_notification_5
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_plan_notification_6
- The variable only takes one (non-missing) value: "1". The variable contains 97.36 % missing observations.
sc_plan_notification_7
- The variable only takes one (non-missing) value: "1". The variable contains 98.9 % missing observations.
sc_plan_notification_8
- The variable only takes one (non-missing) value: "1". The variable contains 98.24 % missing observations.
sc_plan_notification_999
- The variable only takes one (non-missing) value: "1". The variable contains 96.7 % missing observations.
sc_plan_notification_autre
| Variable type |
character |
| Number of missing obs. |
439 (96.7 %) |
| Number of unique values |
15 |
| Mode |
“1/3 temps pour les épreuves” |
- Observed factor levels: "1/3 temps pour les épreuves", "Adaptation des horaires de classe, rattrapage des cours par les camarades, tiers temps supplémentaires aux épreuves", "Aménagement du tiers temps supplémentaires pour les examens", "Ecole spécialisé", "Kine au lycée mais là mdph n?y est pour rien!!", "non port des livres scolaires, salle de repos à disposition", "Pai", "possibilité de sortir de cours, et pour les exams 1/3 temps", "rien", "Soutien scolaire", "Taxi", "tiers temps", "tiers-temps, pause aux toilettes lors d’examens", "un aménagement pendant les examens", "un tiers-temps".
sc_an_debut
| Variable type |
integer |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
52 |
| Median |
1993 |
| 1st and 3rd quartiles |
1986; 1999 |
| Min. and max. |
1950; 2010 |
sc_debut
| Variable type |
integer |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
sc_debut_autre
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“1 ere primaire (belgique)” |
- Observed factor levels: "1 ere primaire (belgique)", "scolarité habituelle en Russie.".
sc_interromp
| Variable type |
integer |
| Number of missing obs. |
64 (14.1 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
sc_redoubl
| Variable type |
integer |
| Number of missing obs. |
54 (11.89 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
sc_type
- The variable only takes one (non-missing) value: "P". The variable contains 11.23 % missing observations.
id_sc_cat
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
2 |
| Mode |
“id_01_sc_02” |
- Observed factor levels: "id_01", "id_01_sc_02".
id_age_cat_2
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
2 |
| Mode |
“Adulte” |
- Observed factor levels: "Adolescent", "Adulte".
id_age_cat_3
| Variable type |
character |
| Number of missing obs. |
26 (5.73 %) |
| Number of unique values |
3 |
| Mode |
“18-29 ans” |
- Observed factor levels: "18-29 ans", "30-39 ans", "40 ans ou plus".
sc_debut_corr
- The variable only takes one (non-missing) value: "1". The variable contains 65.86 % missing observations.
sc_date_debut
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
52 |
| Mode |
“1996-09-01” |
- Observed factor levels: "1950-09-01", "1956-09-01", "1959-09-01", "1960-09-01", "1963-09-01", "1964-09-01", "1965-09-01", "1966-09-01", "1967-09-01", "1968-09-01", "1969-09-01", "1970-09-01", "1971-09-01", "1972-09-01", "1973-09-01", "1974-09-01", "1975-09-01", "1976-09-01", "1977-09-01", "1978-09-01", "1979-09-01", "1980-09-01", "1981-09-01", "1982-09-01", "1983-09-01", "1984-09-01", "1985-09-01", "1986-09-01", "1987-09-01", "1988-09-01", "1989-09-01", "1990-09-01", "1991-09-01", "1992-09-01", "1993-09-01", "1994-09-01", "1995-09-01", "1996-09-01", "1997-09-01", "1998-09-01", "1999-09-01", "2000-09-01", "2001-09-01", "2002-09-01", "2003-09-01", "2004-09-01", "2005-09-01", "2006-09-01", "2007-09-01", "2008-09-01", "2009-09-01", "2010-09-01".
sc_age_debut
| Variable type |
integer |
| Number of missing obs. |
2 (0.44 %) |
| Number of unique values |
3 |
| Median |
6 |
| 1st and 3rd quartiles |
6; 6 |
| Min. and max. |
5; 7 |
sc_etspps
| Variable type |
character |
| Number of missing obs. |
405 (89.21 %) |
| Number of unique values |
3 |
| Mode |
“1-Classe ordinaire” |
- Observed factor levels: "1-Classe ordinaire", "2-CLIS ULIS", "5-Au domicile".
sc_diplome_cat
| Variable type |
character |
| Number of missing obs. |
78 (17.18 %) |
| Number of unique values |
5 |
| Mode |
“5-Diplôme du supérieur long (bac+3,4,5)” |
- Observed factor levels: "1-Sans diplôme ou brevet des collèges", "2-CAP ou BEP", "3-Baccalauréat", "4-Diplôme du supérieur court (bac+2)", "5-Diplôme du supérieur long (bac+3,4,5)".
sc_date_diplome
| Variable type |
character |
| Number of missing obs. |
89 (19.6 %) |
| Number of unique values |
39 |
| Mode |
“2016-06-30” |
- Observed factor levels: "1961-06-30", "1966-06-30", "1972-06-30", "1978-06-30", "1980-06-30", "1983-06-30", "1985-06-30", "1986-06-30", "1987-06-30", "1988-06-30", "1990-06-30", "1991-06-30", "1992-06-30", "1993-06-30", "1994-06-30", "1995-06-30", "1996-06-30", "1997-06-30", "1998-06-30", "1999-06-30", "2000-06-30", "2001-06-30", "2002-06-30", "2003-06-30", "2004-06-30", "2005-06-30", "2006-06-30", "2007-06-30", "2008-06-30", "2009-06-30", "2010-06-30", "2011-06-30", "2012-06-30", "2013-06-30", "2014-06-30", "2015-06-30", "2016-06-30", "2017-06-30", "2018-06-30".
sc_age_diplome
| Variable type |
integer |
| Number of missing obs. |
91 (20.04 %) |
| Number of unique values |
24 |
| Median |
22 |
| 1st and 3rd quartiles |
19; 24 |
| Min. and max. |
13; 38 |
sc_rdb_date_creation
| Variable type |
character |
| Number of missing obs. |
269 (59.25 %) |
| Number of unique values |
121 |
| Mode |
“2017-05-09” |
- Observed factor levels: "2017-04-01", "2017-04-10", "2017-04-12", "2017-04-13", "2017-04-20", "2017-04-22", "2017-04-26", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-16", "2017-05-18", "2017-05-19", "2017-06-15", "2017-06-19", "2017-06-23", "2017-06-27", "2017-06-28", "2017-07-01", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-24", "2017-07-27", "2017-08-10", "2017-08-20", "2017-09-11", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-09", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-13", "2017-12-19", "2017-12-22", "2017-12-27", "2018-01-02", "2018-01-04", "2018-01-23", "2018-01-31", "2018-02-01", "2018-02-08", "2018-02-12", "2018-02-16", "2018-02-20", "2018-02-25", "2018-03-09", "2018-03-12", "2018-03-28", "2018-04-29", "2018-05-03", "2018-05-13", "2018-06-26", "2018-07-26", "2018-08-09", "2018-11-22", "2018-11-24", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-17", "2018-12-20", "2018-12-21", "2018-12-24", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-07", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-17", "2019-01-19", "2019-01-23", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-14", "2019-02-17", "2019-02-26".
sc_rdb_nb
| Variable type |
integer |
| Number of missing obs. |
269 (59.25 %) |
| Number of unique values |
4 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 4 |
sc_rdb_type
- The variable only takes one (non-missing) value: "P". The variable contains 59.25 % missing observations.
sc_rdb_redoubl
- The variable only takes one (non-missing) value: "1". The variable contains 59.47 % missing observations.
id_age_cat
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
2 |
| Mode |
“Adulte” |
- Observed factor levels: "Adolescent", "Adulte".
sc_rdb_cat
- The variable only takes one (non-missing) value: "sc_02_rdb_02". The variable contains 59.25 % missing observations.
sc_rdb01_an
| Variable type |
integer |
| Number of missing obs. |
286 (63 %) |
| Number of unique values |
45 |
| Median |
1998 |
| 1st and 3rd quartiles |
1993; 2006 |
| Min. and max. |
1951; 2018 |
sc_rdb02_an
| Variable type |
integer |
| Number of missing obs. |
402 (88.55 %) |
| Number of unique values |
27 |
| Median |
1999 |
| 1st and 3rd quartiles |
1993; 2008.25 |
| Min. and max. |
1978; 2017 |
sc_rdb03_an
| Variable type |
integer |
| Number of missing obs. |
442 (97.36 %) |
| Number of unique values |
10 |
| Median |
2000 |
| 1st and 3rd quartiles |
1991.75; 2005 |
| Min. and max. |
1987; 2012 |
sc_rdb04_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1993.5 |
| 1st and 3rd quartiles |
1993.25; 1993.75 |
| Min. and max. |
1993; 1994 |
sc_rdb01_cause
| Variable type |
integer |
| Number of missing obs. |
272 (59.91 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
sc_rdb02_cause
| Variable type |
integer |
| Number of missing obs. |
397 (87.44 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
sc_rdb03_cause
| Variable type |
integer |
| Number of missing obs. |
441 (97.14 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
sc_rdb04_cause
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
0.5 |
| 1st and 3rd quartiles |
0.25; 0.75 |
| Min. and max. |
0; 1 |
sc_rdb01_classe
| Variable type |
integer |
| Number of missing obs. |
270 (59.47 %) |
| Number of unique values |
20 |
| Median |
10.5 |
| 1st and 3rd quartiles |
6; 14 |
| Min. and max. |
1; 999 |
sc_rdb02_classe
| Variable type |
integer |
| Number of missing obs. |
397 (87.44 %) |
| Number of unique values |
19 |
| Median |
14 |
| 1st and 3rd quartiles |
9; 19 |
| Min. and max. |
2; 999 |
sc_rdb03_classe
| Variable type |
integer |
| Number of missing obs. |
441 (97.14 %) |
| Number of unique values |
9 |
| Median |
20 |
| 1st and 3rd quartiles |
14; 21 |
| Min. and max. |
9; 999 |
sc_rdb04_classe
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
21 |
| 1st and 3rd quartiles |
20.5; 21.5 |
| Min. and max. |
20; 22 |
sc_rdb01_classe_autre
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Mode |
“2e année d’architecture d’intèrieur” |
- Observed factor levels: "2e année d’architecture d’intèrieur", "BEP en 3 ans", "Paces".
sc_rdb02_classe_autre
- The variable only takes one (non-missing) value: "3e année d’étude d’auxiliaire puéricultrice(belges". The variable contains 99.78 % missing observations.
sc_rdb03_classe_autre
- The variable only takes one (non-missing) value: "5eme année de médecine". The variable contains 99.78 % missing observations.
sc_rdb04_classe_autre
- The variable only takes one value: "NA".
sc_rdb01_classe_cat
| Variable type |
character |
| Number of missing obs. |
270 (59.47 %) |
| Number of unique values |
4 |
| Mode |
“3-Lycée” |
- Observed factor levels: "1-Ecole élémentaire", "2-Collège", "3-Lycée", "4-Supérieur".
sc_rdb02_classe_cat
| Variable type |
character |
| Number of missing obs. |
397 (87.44 %) |
| Number of unique values |
4 |
| Mode |
“3-Lycée” |
- Observed factor levels: "1-Ecole élémentaire", "2-Collège", "3-Lycée", "4-Supérieur".
sc_rdb03_classe_cat
| Variable type |
character |
| Number of missing obs. |
441 (97.14 %) |
| Number of unique values |
3 |
| Mode |
“4-Supérieur” |
- Observed factor levels: "2-Collège", "3-Lycée", "4-Supérieur".
sc_rdb04_classe_cat
- The variable only takes one (non-missing) value: "4-Supérieur". The variable contains 99.56 % missing observations.
sc_int_date_creation
| Variable type |
character |
| Number of missing obs. |
372 (81.94 %) |
| Number of unique values |
61 |
| Mode |
“2017-05-09” |
- Observed factor levels: "2017-04-01", "2017-04-10", "2017-04-11", "2017-04-20", "2017-04-25", "2017-04-26", "2017-04-27", "2017-05-02", "2017-05-03", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-16", "2017-05-19", "2017-05-28", "2017-06-29", "2017-07-05", "2017-07-11", "2017-07-24", "2017-07-27", "2017-10-05", "2017-11-23", "2017-11-24", "2017-11-26", "2017-12-04", "2017-12-11", "2017-12-12", "2017-12-19", "2018-01-02", "2018-01-04", "2018-01-23", "2018-01-27", "2018-02-12", "2018-02-16", "2018-02-20", "2018-03-12", "2018-03-28", "2018-05-03", "2018-06-26", "2018-11-22", "2018-11-30", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-10", "2018-12-11", "2018-12-24", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2019-01-01", "2019-01-06", "2019-01-08", "2019-01-09", "2019-01-14", "2019-01-17", "2019-01-18", "2019-01-23", "2019-02-14".
sc_int_nb
| Variable type |
integer |
| Number of missing obs. |
372 (81.94 %) |
| Number of unique values |
6 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 8 |
sc_int_type
- The variable only takes one (non-missing) value: "P". The variable contains 81.94 % missing observations.
sc_int_interromp
- The variable only takes one (non-missing) value: "1". The variable contains 81.94 % missing observations.
sc_int_cat
- The variable only takes one (non-missing) value: "sc_02_int_02". The variable contains 81.94 % missing observations.
sc_int01_classe
| Variable type |
integer |
| Number of missing obs. |
380 (83.7 %) |
| Number of unique values |
20 |
| Median |
12.5 |
| 1st and 3rd quartiles |
7; 19 |
| Min. and max. |
1; 999 |
sc_int02_classe
| Variable type |
integer |
| Number of missing obs. |
432 (95.15 %) |
| Number of unique values |
14 |
| Median |
13 |
| 1st and 3rd quartiles |
9.75; 21 |
| Min. and max. |
2; 999 |
sc_int03_classe
| Variable type |
integer |
| Number of missing obs. |
444 (97.8 %) |
| Number of unique values |
7 |
| Median |
13 |
| 1st and 3rd quartiles |
12.25; 19.5 |
| Min. and max. |
7; 26 |
sc_int04_classe
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
4 |
| Median |
19 |
| 1st and 3rd quartiles |
13; 21 |
| Min. and max. |
8; 21 |
sc_int05_classe
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
11.5 |
| 1st and 3rd quartiles |
10.25; 12.75 |
| Min. and max. |
9; 14 |
sc_int06_classe
- The variable only takes one (non-missing) value: "12". The variable contains 99.78 % missing observations.
sc_int07_classe
- The variable only takes one (non-missing) value: "13". The variable contains 99.78 % missing observations.
sc_int08_classe
- The variable only takes one (non-missing) value: "14". The variable contains 99.78 % missing observations.
sc_int01_an
| Variable type |
integer |
| Number of missing obs. |
377 (83.04 %) |
| Number of unique values |
30 |
| Median |
2006 |
| 1st and 3rd quartiles |
1996; 2010 |
| Min. and max. |
1951; 2017 |
sc_int02_an
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
15 |
| Median |
2009 |
| 1st and 3rd quartiles |
2001; 2013 |
| Min. and max. |
1983; 2017 |
sc_int03_an
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
7 |
| Median |
2014.5 |
| 1st and 3rd quartiles |
2002.5; 2015.25 |
| Min. and max. |
1987; 2017 |
sc_int04_an
| Variable type |
integer |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Median |
2007.5 |
| 1st and 3rd quartiles |
2003.75; 2012.25 |
| Min. and max. |
2003; 2016 |
sc_int05_an
- The variable only takes one (non-missing) value: "2005". The variable contains 99.78 % missing observations.
sc_int06_an
- The variable only takes one value: "NA".
sc_int07_an
- The variable only takes one value: "NA".
sc_int08_an
- The variable only takes one value: "NA".
sc_int01_an_repr
| Variable type |
integer |
| Number of missing obs. |
384 (84.58 %) |
| Number of unique values |
30 |
| Median |
2005 |
| 1st and 3rd quartiles |
1996.25; 2011 |
| Min. and max. |
1952; 2017 |
sc_int02_an_repr
| Variable type |
integer |
| Number of missing obs. |
434 (95.59 %) |
| Number of unique values |
16 |
| Median |
2011 |
| 1st and 3rd quartiles |
2001.5; 2015 |
| Min. and max. |
1983; 2017 |
sc_int03_an_repr
| Variable type |
integer |
| Number of missing obs. |
447 (98.46 %) |
| Number of unique values |
6 |
| Median |
2014 |
| 1st and 3rd quartiles |
2002.5; 2017 |
| Min. and max. |
1989; 2018 |
sc_int04_an_repr
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2004 |
| 1st and 3rd quartiles |
2003.5; 2004.5 |
| Min. and max. |
2003; 2005 |
sc_int05_an_repr
- The variable only takes one (non-missing) value: "2006". The variable contains 99.78 % missing observations.
sc_int06_an_repr
- The variable only takes one value: "NA".
sc_int07_an_repr
- The variable only takes one value: "NA".
sc_int08_an_repr
- The variable only takes one value: "NA".
sc_int01_cause_1
- The variable only takes one (non-missing) value: "1". The variable contains 94.93 % missing observations.
sc_int02_cause_1
- The variable only takes one (non-missing) value: "1". The variable contains 98.46 % missing observations.
sc_int03_cause_1
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_int04_cause_1
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_int05_cause_1
- The variable only takes one value: "NA".
sc_int06_cause_1
- The variable only takes one value: "NA".
sc_int07_cause_1
- The variable only takes one value: "NA".
sc_int08_cause_1
- The variable only takes one value: "NA".
sc_int01_cause_2
- The variable only takes one (non-missing) value: "1". The variable contains 89.21 % missing observations.
sc_int02_cause_2
- The variable only takes one (non-missing) value: "1". The variable contains 96.92 % missing observations.
sc_int03_cause_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
sc_int04_cause_2
- The variable only takes one value: "NA".
sc_int05_cause_2
- The variable only takes one value: "NA".
sc_int06_cause_2
- The variable only takes one value: "NA".
sc_int07_cause_2
- The variable only takes one value: "NA".
sc_int08_cause_2
- The variable only takes one value: "NA".
sc_int01_cause_3
- The variable only takes one (non-missing) value: "1". The variable contains 94.05 % missing observations.
sc_int02_cause_3
- The variable only takes one (non-missing) value: "1". The variable contains 98.46 % missing observations.
sc_int03_cause_3
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_int04_cause_3
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_int05_cause_3
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_int06_cause_3
- The variable only takes one value: "NA".
sc_int07_cause_3
- The variable only takes one value: "NA".
sc_int08_cause_3
- The variable only takes one value: "NA".
sc_int01_cause_4
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
sc_int02_cause_4
- The variable only takes one value: "NA".
sc_int03_cause_4
- The variable only takes one value: "NA".
sc_int04_cause_4
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_int05_cause_4
- The variable only takes one value: "NA".
sc_int06_cause_4
- The variable only takes one value: "NA".
sc_int07_cause_4
- The variable only takes one value: "NA".
sc_int08_cause_4
- The variable only takes one value: "NA".
sc_int01_cause_999
- The variable only takes one (non-missing) value: "1". The variable contains 95.15 % missing observations.
sc_int02_cause_999
- The variable only takes one (non-missing) value: "1". The variable contains 98.68 % missing observations.
sc_int03_cause_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
sc_int04_cause_999
- The variable only takes one value: "NA".
sc_int05_cause_999
- The variable only takes one value: "NA".
sc_int06_cause_999
- The variable only takes one value: "NA".
sc_int07_cause_999
- The variable only takes one value: "NA".
sc_int08_cause_999
- The variable only takes one value: "NA".
sc_int01_cause_autre
| Variable type |
character |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
21 |
| Mode |
“6 mois en maison de santé de La Croix Rouge” |
- Observed factor levels: "6 mois en maison de santé de La Croix Rouge", "Antibiotiques en perfusion prévu initialement pour 3 mois", "Attente de greffe", "Besoin de repos a cause d?anxiete", "cure thermale", "CURES PERFUSIONS ANTIBIOTIQUES", "Décès de mon frère entraînant une aggravation de mon état de santé, conduisant à la greffe", "Décompensation", "DEPRESSION PHOBIE SOCIALE /SCOLAIRE", "diagnostic tardif de la muco et début du diabète", "douleurs non reconnue dû à la muco", "état dépressif et perfusion très fréquente (1/ mois)", "fatigue", "Grippe", "Hospitalisation a domicile cure antibiotiques par intraveineuse", "Oxygénothérapie, dégradation de l’état général..", "perfusion", "prevention Grippe H1N1", "Stage à l’étranger obligatoire non compatible avec ma santé", "traitements lourds par intraveineuses contre pyocianique", "Trop fatiguée pour poursuivre".
sc_int02_cause_autre
| Variable type |
character |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Mode |
“Dépression” |
- Observed factor levels: "Dépression", "Hospitalisation cure antibiotiques par voie intraveineuse", "Hospitalisation Psychatrique", "hypertension, grippe etc…", "infection traitement par perfusion à la maison", "projet greffe".
sc_int03_cause_autre
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Mode |
“anemie, douleurs thoraciques, dépression” |
- Observed factor levels: "anemie, douleurs thoraciques, dépression", "Cas de grippe HAN1", "fatigue".
sc_int04_cause_autre
- The variable only takes one value: "NA".
sc_int05_cause_autre
- The variable only takes one value: "NA".
sc_int06_cause_autre
- The variable only takes one value: "NA".
sc_int07_cause_autre
- The variable only takes one value: "NA".
sc_int08_cause_autre
- The variable only takes one value: "NA".
sc_int01_classe_autre
| Variable type |
character |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Mode |
“2e année d’architecture d’intèrieur” |
- Observed factor levels: "2e année d’architecture d’intèrieur", "3eme année préparation CAP cuisine hôtelerie", "5eme année de médecine", "BEP".
sc_int02_classe_autre
- The variable only takes one (non-missing) value: "BEP Agent Administratif des Entreprises". The variable contains 99.78 % missing observations.
sc_int03_classe_autre
- The variable only takes one value: "NA".
sc_int04_classe_autre
- The variable only takes one value: "NA".
sc_int05_classe_autre
- The variable only takes one value: "NA".
sc_int06_classe_autre
- The variable only takes one value: "NA".
sc_int07_classe_autre
- The variable only takes one value: "NA".
sc_int08_classe_autre
- The variable only takes one value: "NA".
sc_int01_classe_repr
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
19 |
| Median |
13 |
| 1st and 3rd quartiles |
7; 20 |
| Min. and max. |
1; 999 |
sc_int02_classe_repr
| Variable type |
integer |
| Number of missing obs. |
432 (95.15 %) |
| Number of unique values |
13 |
| Median |
13 |
| 1st and 3rd quartiles |
12; 19.75 |
| Min. and max. |
2; 999 |
sc_int03_classe_repr
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
7 |
| Median |
13.5 |
| 1st and 3rd quartiles |
12; 22 |
| Min. and max. |
7; 26 |
sc_int04_classe_repr
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
17 |
| 1st and 3rd quartiles |
15; 19 |
| Min. and max. |
13; 21 |
sc_int05_classe_repr
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
11.5 |
| 1st and 3rd quartiles |
10.25; 12.75 |
| Min. and max. |
9; 14 |
sc_int06_classe_repr
- The variable only takes one (non-missing) value: "12". The variable contains 99.78 % missing observations.
sc_int07_classe_repr
- The variable only takes one (non-missing) value: "13". The variable contains 99.78 % missing observations.
sc_int08_classe_repr
- The variable only takes one (non-missing) value: "14". The variable contains 99.78 % missing observations.
sc_int01_classe_repr_autre
| Variable type |
character |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Mode |
“2e année d’architecture d’intèrieur” |
- Observed factor levels: "2e année d’architecture d’intèrieur", "3eme année préparation CAP cuisine hôtelerie", "5eme année de médecine", "BAC PRO SECRETARIAT VALIDATION DES ACQUIS PROFESS", "BEP", "CAP esthétique".
sc_int02_classe_repr_autre
- The variable only takes one (non-missing) value: "BEP Agent Administratif des Entreprises". The variable contains 99.78 % missing observations.
sc_int03_classe_repr_autre
- The variable only takes one value: "NA".
sc_int04_classe_repr_autre
- The variable only takes one value: "NA".
sc_int05_classe_repr_autre
- The variable only takes one value: "NA".
sc_int06_classe_repr_autre
- The variable only takes one value: "NA".
sc_int07_classe_repr_autre
- The variable only takes one value: "NA".
sc_int08_classe_repr_autre
- The variable only takes one value: "NA".
sc_int01_interromp_duree
| Variable type |
integer |
| Number of missing obs. |
378 (83.26 %) |
| Number of unique values |
7 |
| Median |
3 |
| 1st and 3rd quartiles |
1.75; 7 |
| Min. and max. |
1; 7 |
sc_int02_interromp_duree
| Variable type |
integer |
| Number of missing obs. |
432 (95.15 %) |
| Number of unique values |
6 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 7 |
| Min. and max. |
1; 7 |
sc_int03_interromp_duree
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
4 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 7 |
| Min. and max. |
1; 7 |
sc_int04_interromp_duree
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Median |
4 |
| 1st and 3rd quartiles |
2.5; 5.5 |
| Min. and max. |
1; 7 |
sc_int05_interromp_duree
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
6 |
| 1st and 3rd quartiles |
5.5; 6.5 |
| Min. and max. |
5; 7 |
sc_int06_interromp_duree
- The variable only takes one (non-missing) value: "6". The variable contains 99.78 % missing observations.
sc_int07_interromp_duree
- The variable only takes one (non-missing) value: "7". The variable contains 99.78 % missing observations.
sc_int08_interromp_duree
- The variable only takes one (non-missing) value: "7". The variable contains 99.78 % missing observations.
sc_int01_mois
| Variable type |
integer |
| Number of missing obs. |
387 (85.24 %) |
| Number of unique values |
11 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 10 |
| Min. and max. |
1; 12 |
sc_int02_mois
| Variable type |
integer |
| Number of missing obs. |
434 (95.59 %) |
| Number of unique values |
8 |
| Median |
9 |
| 1st and 3rd quartiles |
3; 10.25 |
| Min. and max. |
1; 12 |
sc_int03_mois
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
5 |
| Median |
11 |
| 1st and 3rd quartiles |
8.75; 12 |
| Min. and max. |
3; 12 |
sc_int04_mois
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
2 |
| Median |
11 |
| 1st and 3rd quartiles |
10.5; 11 |
| Min. and max. |
10; 11 |
sc_int05_mois
- The variable only takes one (non-missing) value: "12". The variable contains 99.78 % missing observations.
sc_int06_mois
- The variable only takes one value: "NA".
sc_int07_mois
- The variable only takes one value: "NA".
sc_int08_mois
- The variable only takes one value: "NA".
sc_int01_mois_repr
| Variable type |
integer |
| Number of missing obs. |
387 (85.24 %) |
| Number of unique values |
11 |
| Median |
9 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
sc_int02_mois_repr
| Variable type |
integer |
| Number of missing obs. |
435 (95.81 %) |
| Number of unique values |
9 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 9.5 |
| Min. and max. |
1; 12 |
sc_int03_mois_repr
| Variable type |
integer |
| Number of missing obs. |
447 (98.46 %) |
| Number of unique values |
7 |
| Median |
6 |
| 1st and 3rd quartiles |
4; 9.5 |
| Min. and max. |
1; 11 |
sc_int04_mois_repr
- The variable only takes one (non-missing) value: "5". The variable contains 99.78 % missing observations.
sc_int05_mois_repr
- The variable only takes one (non-missing) value: "5". The variable contains 99.78 % missing observations.
sc_int06_mois_repr
- The variable only takes one value: "NA".
sc_int07_mois_repr
- The variable only takes one value: "NA".
sc_int08_mois_repr
- The variable only takes one value: "NA".
sc_int01_non_repr
- The variable only takes one (non-missing) value: "3". The variable contains 99.34 % missing observations.
sc_int02_non_repr
- The variable only takes one (non-missing) value: "3". The variable contains 99.78 % missing observations.
sc_int03_non_repr
- The variable only takes one (non-missing) value: "3". The variable contains 99.78 % missing observations.
sc_int04_non_repr
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
sc_int05_non_repr
- The variable only takes one value: "NA".
sc_int06_non_repr
- The variable only takes one value: "NA".
sc_int07_non_repr
- The variable only takes one value: "NA".
sc_int08_non_repr
- The variable only takes one value: "NA".
sc_int01_repr
| Variable type |
integer |
| Number of missing obs. |
372 (81.94 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
sc_int02_repr
| Variable type |
integer |
| Number of missing obs. |
431 (94.93 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
sc_int03_repr
| Variable type |
integer |
| Number of missing obs. |
444 (97.8 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
sc_int04_repr
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
sc_int05_repr
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
sc_int06_repr
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_int07_repr
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_int08_repr
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
sc_int01_classe_cat
| Variable type |
character |
| Number of missing obs. |
380 (83.7 %) |
| Number of unique values |
4 |
| Mode |
“4-Supérieur” |
- Observed factor levels: "1-Ecole élémentaire", "2-Collège", "3-Lycée", "4-Supérieur".
sc_int02_classe_cat
| Variable type |
character |
| Number of missing obs. |
432 (95.15 %) |
| Number of unique values |
4 |
| Mode |
“4-Supérieur” |
- Observed factor levels: "1-Ecole élémentaire", "2-Collège", "3-Lycée", "4-Supérieur".
sc_int03_classe_cat
| Variable type |
character |
| Number of missing obs. |
444 (97.8 %) |
| Number of unique values |
3 |
| Mode |
“4-Supérieur” |
- Observed factor levels: "2-Collège", "3-Lycée", "4-Supérieur".
sc_int04_classe_cat
| Variable type |
character |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
2 |
| Mode |
“4-Supérieur” |
- Observed factor levels: "2-Collège", "4-Supérieur".
sc_int05_classe_cat
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“2-Collège” |
- Observed factor levels: "2-Collège", "4-Supérieur".
sc_int06_classe_cat
- The variable only takes one (non-missing) value: "3-Lycée". The variable contains 99.78 % missing observations.
sc_int07_classe_cat
- The variable only takes one (non-missing) value: "4-Supérieur". The variable contains 99.78 % missing observations.
sc_int08_classe_cat
- The variable only takes one (non-missing) value: "4-Supérieur". The variable contains 99.78 % missing observations.
sc_int01_classerepr_cat
| Variable type |
character |
| Number of missing obs. |
417 (91.85 %) |
| Number of unique values |
3 |
| Mode |
“2-Collège” |
- Observed factor levels: "1-Ecole élémentaire", "2-Collège", "3-Lycée".
sc_int02_classerepr_cat
| Variable type |
character |
| Number of missing obs. |
444 (97.8 %) |
| Number of unique values |
3 |
| Mode |
“3-Lycée” |
- Observed factor levels: "1-Ecole élémentaire", "2-Collège", "3-Lycée".
sc_int03_classerepr_cat
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
2 |
| Mode |
“3-Lycée” |
- Observed factor levels: "2-Collège", "3-Lycée".
sc_int04_classerepr_cat
- The variable only takes one value: "NA".
sc_int05_classerepr_cat
- The variable only takes one (non-missing) value: "2-Collège". The variable contains 99.78 % missing observations.
sc_int06_classerepr_cat
- The variable only takes one (non-missing) value: "3-Lycée". The variable contains 99.78 % missing observations.
sc_int07_classerepr_cat
- The variable only takes one value: "NA".
sc_int08_classerepr_cat
- The variable only takes one value: "NA".
sc_int01_duree_j
| Variable type |
numeric |
| Number of missing obs. |
378 (83.26 %) |
| Number of unique values |
7 |
| Median |
53.27 |
| 1st and 3rd quartiles |
36.15; 106.54 |
| Min. and max. |
30.44; 106.54 |
sc_int02_duree_j
| Variable type |
numeric |
| Number of missing obs. |
432 (95.15 %) |
| Number of unique values |
6 |
| Median |
53.27 |
| 1st and 3rd quartiles |
38.05; 106.54 |
| Min. and max. |
30.44; 106.54 |
sc_int03_duree_j
| Variable type |
numeric |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
4 |
| Median |
95.12 |
| 1st and 3rd quartiles |
53.27; 106.54 |
| Min. and max. |
30.44; 106.54 |
sc_int04_duree_j
| Variable type |
numeric |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Median |
68.49 |
| 1st and 3rd quartiles |
49.46; 87.52 |
| Min. and max. |
30.44; 106.54 |
sc_int05_duree_j
| Variable type |
numeric |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
95.12 |
| 1st and 3rd quartiles |
89.42; 100.83 |
| Min. and max. |
83.71; 106.54 |
sc_int06_duree_j
- The variable only takes one (non-missing) value: "98.93". The variable contains 99.78 % missing observations.
sc_int07_duree_j
- The variable only takes one (non-missing) value: "106.54". The variable contains 99.78 % missing observations.
sc_int08_duree_j
- The variable only takes one (non-missing) value: "106.54". The variable contains 99.78 % missing observations.
pr_date_creation
| Variable type |
character |
| Number of missing obs. |
71 (15.64 %) |
| Number of unique values |
193 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-07", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-13", "2017-04-19", "2017-04-20", "2017-04-22", "2017-04-25", "2017-04-26", "2017-04-27", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-16", "2017-05-17", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-24", "2017-06-01", "2017-06-02", "2017-06-13", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-22", "2017-06-23", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-24", "2017-07-25", "2017-07-27", "2017-08-01", "2017-08-03", "2017-08-08", "2017-08-10", "2017-08-14", "2017-08-17", "2017-08-20", "2017-08-30", "2017-09-06", "2017-09-07", "2017-09-11", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-05", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-09", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-28", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-21", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-23", "2018-01-27", "2018-01-30", "2018-01-31", "2018-02-01", "2018-02-04", "2018-02-08", "2018-02-11", "2018-02-12", "2018-02-14", "2018-02-16", "2018-02-20", "2018-02-25", "2018-02-26", "2018-03-09", "2018-03-11", "2018-03-12", "2018-03-28", "2018-04-16", "2018-04-29", "2018-05-03", "2018-05-04", "2018-05-13", "2018-05-16", "2018-05-31", "2018-06-26", "2018-07-26", "2018-07-30", "2018-08-09", "2018-09-04", "2018-09-11", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-26", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-01", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-05", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-20", "2018-12-21", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-15", "2019-01-16", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-21", "2019-01-22", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-03", "2019-02-14", "2019-02-17", "2019-02-18", "2019-02-26", "2019-02-27", "2019-03-03", "2019-03-11".
pr_pdt_6mois_ado
- The variable only takes one (non-missing) value: "0". The variable contains 94.93 % missing observations.
pr_prems_an
| Variable type |
integer |
| Number of missing obs. |
191 (42.07 %) |
| Number of unique values |
40 |
| Median |
2007 |
| 1st and 3rd quartiles |
2001; 2012.5 |
| Min. and max. |
1961; 2018 |
pr_benef_an
| Variable type |
integer |
| Number of missing obs. |
415 (91.41 %) |
| Number of unique values |
18 |
| Median |
2009 |
| 1st and 3rd quartiles |
2006; 2014 |
| Min. and max. |
1994; 2018 |
pr_benef_hand
| Variable type |
integer |
| Number of missing obs. |
282 (62.11 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
pr_benef_mesures
| Variable type |
integer |
| Number of missing obs. |
282 (62.11 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
pr_hand_demande
| Variable type |
integer |
| Number of missing obs. |
323 (71.15 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
pr_interromp
| Variable type |
integer |
| Number of missing obs. |
182 (40.09 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
pr_mesures_1
- The variable only takes one (non-missing) value: "1". The variable contains 92.07 % missing observations.
pr_mesures_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_mesures_3
- The variable only takes one (non-missing) value: "1". The variable contains 98.46 % missing observations.
pr_mesures_7
- The variable only takes one (non-missing) value: "1". The variable contains 98.68 % missing observations.
pr_mesures_11
- The variable only takes one (non-missing) value: "1". The variable contains 96.04 % missing observations.
pr_mesures_12
- The variable only takes one (non-missing) value: "1". The variable contains 94.05 % missing observations.
pr_mesures_13
- The variable only takes one (non-missing) value: "1". The variable contains 97.8 % missing observations.
pr_mesures_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
pr_mesures_autre
| Variable type |
character |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Mode |
“pension invalidite” |
- Observed factor levels: "pension invalidite", "Restriction géographique du poste (pas hors aggolmération et sa périphérie élargie). Restriction sur le type de poste (pas en maternelle)", "titularisation sans passer de concours", "Un recrutement BOE pour devenir titulaire fonctionnaire".
pr_mesures_autre_bis
| Variable type |
character |
| Number of missing obs. |
417 (91.85 %) |
| Number of unique values |
24 |
| Mode |
“RQTH” |
- Observed factor levels: "AEH", "Auto entreprise", "carte de stationnement", "Carte de stationnement", "Carte de Stationnement", "Carte de stationnement européenne", "Carte de stationnement handicapé", "DEMANDE DE CRP EN 05/2018 EN ATTENTE", "EN ATTENTE", "macaron pour le stationnement PMR", "macaron stationnement", "macaron stationnement GIC", "NOTIFICATION TRAVAILLEUR HANDICAPEE", "Qualité de travailleur handicapé", "reconnaisance travailleur handicapé (RQTH)", "reconnaissance handicap à 80%", "Reconnaissance travailleur handicapé", "Reconnaissance travailleur Handicapé", "Rien", "RQTH", "RQTH - Carte de Stationnement", "stationement", "Travailleur Handicapé", "Travailleur handicapée".
pr_mesures_bis_10
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_mesures_bis_11
- The variable only takes one (non-missing) value: "1". The variable contains 76.21 % missing observations.
pr_mesures_bis_12
- The variable only takes one (non-missing) value: "1". The variable contains 76.87 % missing observations.
pr_mesures_bis_13
- The variable only takes one (non-missing) value: "1". The variable contains 86.56 % missing observations.
pr_mesures_bis_999
- The variable only takes one (non-missing) value: "1". The variable contains 91.85 % missing observations.
pr_pdt_6mois
| Variable type |
integer |
| Number of missing obs. |
97 (21.37 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
pr_prems_cadre
| Variable type |
integer |
| Number of missing obs. |
183 (40.31 %) |
| Number of unique values |
5 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_prems_cadre_autre
| Variable type |
character |
| Number of missing obs. |
438 (96.48 %) |
| Number of unique values |
16 |
| Mode |
“alternance” |
- Observed factor levels: "alternance", "artiste / directeur artistique", "bureau d’expert comptable, ensuite opératrice de s", "cabinet dentaire", "chantier et atelier", "Chez des particuliers en intérieur ou extérieur", "Dans une banque", "ETRANGER- CHEZ MES CLIENTS EN ENTREPRISES", "Fabrique de meubles(’avec pdts cellulosiques)", "garde d’enfant à mon domicile", "hopital", "Maison de retraite", "Moitié sur le terrain moitié dans les bureaux", "quincaillerie", "Travail à l’école et accompagnement jusqu’à la can", "Vente en téléphonie".
pr_prems_contrat
| Variable type |
integer |
| Number of missing obs. |
194 (42.73 %) |
| Number of unique values |
8 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 3 |
| Min. and max. |
1; 999 |
pr_prems_contrat_autre
| Variable type |
character |
| Number of missing obs. |
440 (96.92 %) |
| Number of unique values |
13 |
| Mode |
“Apprentissage” |
- Observed factor levels: "Apprentissage", "CAE", "CAE (Contrat d’Accompagnement dans l’Emploi)", "Contrat avenir", "contrat doctoral", "Contrat emploi jeune (2002-2004)", "CUI", "fonctionnaire", "Fonctionnaire stagiaire", "Récrutement COTOREP (donc titularisable au terme de la 1ère année)", "remplacement salarié", "salariée à caractère discontinu rémunérée au cachet", "Service civique".
pr_prems_entreprise
| Variable type |
integer |
| Number of missing obs. |
193 (42.51 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_prems_prof
| Variable type |
character |
| Number of missing obs. |
182 (40.09 %) |
| Number of unique values |
255 |
| Mode |
“assistante de direction” |
- Observed factor levels: "Accompagnante d’élève en situation de handicap", "Accompagnement en cantine scolaire", "Adjoint administratif", "ADJOINT ADMINISTRATIF", "Adjointe au conservateur du service historique de la marine à Cherbourg", "Agent administratif", "AGENT ADMINISTRATIF", "Agent commercial spécialisé SNCF", "agent d’assistance aéroportuaire", "agent d’exploitation", "Agent de maintenance", "agent de production", "Agent de service hospitalier", "Agent de service hospitalier de nuit en EHPAD", "Agent de tri à La Poste", "Agent de tri en bureautique", "agent hospitalier", "agent maitrise grande distribution", "AGENT PRODUCTION INDUSTRIE", "Aide assistante maternelle", "AIDE BIBLIOTHECAIRE", "Aide-éducateur en école maternelle publique.", "Aide-soignante en gériatrie", "Alternant Qualité Sécurité Environnement", "Ambulancier", "Analyste Marketing Stratégique", "analyste titrisation dans une banque d’investissement", "Animatrice socioculturelle", "Année de césure en tant qu’élève ingénieur", "apprenti comptable", "Apprenti dans un centre de gestion agréé", "Apprenti manager de rayon", "Apprentissage ingénieur d’etudes en biologie", "Apprentissage vendeuse", "Architecte", "artiste de cirque", "artiste musicienne", "Assistant", "assistant administratif", "assistant comptable", "ASSISTANT COMPTABLE", "Assistant d’Education", "Assistant d’éducation", "Assistant d’Education dans un Lycée", "assistant de conservateur", "Assistant de Paie", "Assistant Nouvelle Technologies", "assistant social du personnel", "Assistante administratif", "Assistante commerciale", "Assistante d’édition", "Assistante d’une agence de services à la personne", "assistante de direction", "ASSISTANTE DE DIRECTION", "Assistante de Direction au Conseil Régional", "assistante de gestion", "Assistante de Gestion", "Assistante maternelle agréée", "Assistante Ressources Humaines", "Assitante commerciale et comptable", "Assitante de direction dans le cadre du BTS", "Attaché commercial", "Auditeur financier", "Auto entrepreneur", "Auxiliaire de puériculture", "Auxiliaire de Vie Scolaire", "avocat", "AVS", "Bagagiste aéroport Orly", "Barmaid", "Caissière", "Caissière employée libre service chez Lidl", "Cartographe", "CHARGE COMMERCIAL", "chargé d’affaires", "chargé d’étude", "Chargé d’études environnement", "Chargé de clientèle", "Chargé de mission", "Chargé de recrutement", "chargée d’études géotechniques et risques naturels", "Chargée de communication", "chargée de mission", "Chargée de mission", "Chargée de missions sur un projet informatique", "Chargée de projets", "Chargée de webmarketing", "Chef de projet en informatique industrielle", "chercheur", "Chercheur-doctorant en France", "chirurgien dentiste", "COIFFEUSE", "Commercial", "COMMERCIAL", "Commercial dans une société fabricante de matériel pour l’analyse agroalimentaire", "Commercial-métreur", "comptable", "Comptable", "Conseil en gestion comptabilité des entreprises", "Conseiller clientèle banque", "Conseiller de vente bricolage technique", "Conseillère accueil dans le secteur bancaire", "Conseillère commerciale en assurance", "Conseillère de vente", "CONSEILLERE EN INSERTION", "Conseillere en insertion professionnelle", "Conseillère en séjour en office de tourisme", "Consolideur, comptabilité internationale", "Consultant informatique (développement)", "Consultant testing", "CONSULTANTE", "contractuel à l’inpection académique", "Contrat doctoral", "Contrôle qualité", "Contrôleur de gestion", "Contrôleur de Gestion", "Contrôleur de gestion industriel", "Contrôleur des impots", "contrôleuse congés spectacles", "coupeur de textile", "Docteur en pharmacie", "Doctorant salarié", "Doctorante", "educateur sportif", "Educateur sportif", "Electricien", "Électricien en bâtiment", "Electricité Industrielle", "Employé de commerce", "employe de rayon", "Employe libre service", "Employé polyvalent en restauration", "Employée à France Télécom", "Employée de bureau", "employer commercial", "en usine", "enseignant", "Enseignant", "ENSEIGNANT-CHERCHEUR", "enseignante", "Equipier Polyvalent chez Mac Donald’s", "esthéticienne", "Esthéticienne", "expert d’assurance", "externe en médecine", "Fleuriste", "Garde d?enfant", "Garde d’enfants", "Hôtesse d’accueil en salons VIP aéroport", "hotesse de caisse", "Hôtesse de caisse", "HÖTESSE DE CAISSE", "infirmiere", "Infirmiere", "Infirmière puis cadre de santé", "informaticien", "Ingenieur", "Ingénieur", "Ingénieur alternant", "Ingénieur commercial", "Ingénieur commerciale", "Ingenieur d essais dans l’industrie", "INgenieur d’étude", "Ingénieur de recherche contractuel au CNRS", "Ingénieur en bureau études structure bâtiment", "ingénieur en conception électronique", "Ingénieur en informatique", "Ingénieur étude et développement", "Ingénieur études", "Ingénieur informatique", "ingénieur procédés", "Ingénieur Radio", "Ingénieur télécom et informatique", "Ingénieur travaux publics dans une entreprise privée", "institutrice", "Job saisonnier ou replacements", "Juriste", "kinésithérapeute", "Logidticien", "Logisticien chez AIR France Industrie", "magasinier", "MAGASINIER", "Maîtresse de Maison de Retraite", "Manipulateur en électroradiologie", "manipulatrice en radiotherapie", "mecanicien automobile", "Mécanicien Tourneur Fraiseur", "Médiatrice culturelle", "militaire", "Monitrice auto-école", "monitrice educatrice", "monteur mecanicien", "monteur sur chaine automatisée", "Opérateur DT DICT", "opticien lunetier", "opticienne", "Orthophoniste", "Ouvrier paysagiste", "ouvriere pépiniériste", "patissiere", "Pharmacien", "Préparateur de commandes", "Preparatrice en pharmacie", "PROFESSEUR DES ECOLES", "professeur des écoles", "Professeur particulier", "professeure des écoles", "Prothesiste dentaire", "Réceptionniste dans un hôtel", "Réceptionniste de commande", "RESPONSABLE COMPTABLE DANS UN GRAND GROUPE DE BTP", "Responsable de Secteur", "Sage-femme", "secretaire", "secrétaire", "Secrétaire", "Secrétaire au plan départemental d insertion des travailleurs handicapés", "SECRETAIRE COMPTABLE", "Secrétaire comptable", "secrétaire dans un lycée public", "Secrétaire dans une écolé privée", "secrétaire de rédaction", "SECRETAIRE DOCUMENTALISTE DANS UN IFSI", "SECRETAIRE FONCTION PUBLIQUE (DDE)", "secrétaire gestionnaire de laboratoire au CNRS", "SECRETAIRE MEDICALE", "secrétaire médicale", "serveuse", "Serveuse", "Service civique 8 mois au centre impots", "surveillante en collège", "technicien", "Technicien d’installations climatiques", "technicien de laboratoire", "Technicien de maintenance audiovisuel", "Technicien de maintenance électronique grand public", "technicien en alternance", "Technicien Qualité", "technicienne de laboratoire", "Technicienne de laboratoires", "TECHNICIENNE METHODES", "TECHNICO-COMMERCIAL", "VACATAIRE ADMINISTRATIF A L’UNIVERSITE", "Vendeur", "Vendeur disque", "VENDEUR ELECTRODOMESTIQUE MULTIMEDIA", "Vendeur FNAC", "Vendeuse", "Vendeuse d’articles de sport dans un grand magasin de sport", "Vendeuse en BEP VENTE ACTION MARCHANDE MAROQUINERIE", "Vendeuse en pret à porter", "Vendeuse en prêt à porter", "Vendeuse en téléphonie contrat e alternance de 2 ans", "Voiture pilote convois exceptionnels", "Volontaire en service civique".
pr_prems_qual
| Variable type |
integer |
| Number of missing obs. |
210 (46.26 %) |
| Number of unique values |
9 |
| Median |
7 |
| 1st and 3rd quartiles |
5; 9 |
| Min. and max. |
1; 9 |
pr_prems_statut
| Variable type |
integer |
| Number of missing obs. |
183 (40.31 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 3 |
pr_prems_temps
| Variable type |
integer |
| Number of missing obs. |
184 (40.53 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1.75 |
| Min. and max. |
1; 2 |
pr_sap_an_prof
| Variable type |
integer |
| Number of missing obs. |
287 (63.22 %) |
| Number of unique values |
26 |
| Median |
2013 |
| 1st and 3rd quartiles |
2008.5; 2016 |
| Min. and max. |
1987; 2018 |
pr_sap_cadre
| Variable type |
integer |
| Number of missing obs. |
289 (63.66 %) |
| Number of unique values |
5 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_sap_cadre_autre
| Variable type |
character |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
5 |
| Mode |
“Boutique” |
- Observed factor levels: "Boutique", "Client en intérieur ou extérieur", "En partie télétravail à domicile/ en partie au bureau", "hospitalier", "Sur un centre d’appel plus en contact direct avec la clientèle. Travail en équipe mais que par téléphone.".
pr_sap_contrat
| Variable type |
integer |
| Number of missing obs. |
295 (64.98 %) |
| Number of unique values |
5 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 999 |
pr_sap_contrat_autre
| Variable type |
character |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
7 |
| Mode |
“CAE” |
- Observed factor levels: "Bénéficiaire de l’obligation d’emploi", "CAE", "CUI (Contrat Unique d’Insertion)", "fonctionnaire", "Fonctionnaire", "fonctionnaire titulaire", "Fonctionnaire titulaire".
pr_sap_emploi
| Variable type |
integer |
| Number of missing obs. |
92 (20.26 %) |
| Number of unique values |
9 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 6 |
| Min. and max. |
1; 999 |
pr_sap_emploi_autre
| Variable type |
character |
| Number of missing obs. |
427 (94.05 %) |
| Number of unique values |
27 |
| Mode |
“Arret de travail” |
- Observed factor levels: "Arret de travail", "Arrêt longue durée", "Arrêt maladie attentes de greffes", "ARRET MALADIE DEPUIS 04/2017", "Arrêt maladie longue durée", "Auteur (à la maison)", "CHEF D’ENTREPRISE", "citl", "Congé longue durée", "Congé longue maladie", "EMPLOYE A 75 % + PENSION INVALIDITE 1ERE CATEGORIE", "en apprentissage", "en arrêt maladie", "EN ATTENTE INVALIDITE", "En fin de service civique et recherche d’emploi", "en reconversion professionnelle", "Etat de santé incompatible avec un emploi", "je dirige ma propre société (SAS et auto-entrep)", "maladie professionelle", "Ne travaille pas sans recherche sans chomage", "Petits boulots, bénévolat, études d’art-thérapie", "Projet de création d?entreprise", "régisseur son intermittent du spectacle", "rétablissement après greffe, recherche projet", "sans emploi", "Sans emploi", "Service civique".
pr_sap_entreprise
| Variable type |
integer |
| Number of missing obs. |
296 (65.2 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_sap_prof
| Variable type |
character |
| Number of missing obs. |
284 (62.56 %) |
| Number of unique values |
163 |
| Mode |
“Pharmacien” |
- Observed factor levels: "Accompagnant des Elèves en Situation de Handicap", "Accompagnante d’élève en situation de handicap", "Adjoint administratif", "Adjoint Administratif", "ADJOINT ADMINISTRATIF DE L’EDUCATION NATIONALE", "Agent administratif", "agent d’accueil aéroportuaire", "Aide administrative au domicile des personnes âgées", "Ambulancier", "analyste risque de crédit dans une banque", "Animateur jeunesse", "artiste / Directeur artistique", "assistant administratif", "Assistant administratif", "Assistante administratif", "Assistante administrative", "Assistante d’édition et infographiste", "assistante de direction", "Assistante sociale", "ATSEM", "Audioprothésiste", "Auditeur énergétique", "Auto entrepreneur dans la vente sur internet", "Auxiliaire de puériculture en maternité", "Baby-sitter", "Barman", "Cadre", "Cadre de management La Poste", "Cadre de santé", "chargé d’étude", "Chargé de clientèle", "Chargé de gestion commerciale", "Chargé de mission", "chargée d’études en ingénierie routière", "Chargée de Marketing Digital", "chargée de mission", "Chargée de mission", "Chargée de projets", "Chargée des publics", "chef d’entreprise", "Chef de projet en informatique de gestion", "Chef de Réception", "Chef de Secteur commerce bricolage", "chef de service", "chef de service études électroniques", "Cheffe du bureau de l’immobilier et du logement/ région Normandie de gendarmerie", "chercheur", "chirurgien dentiste", "Commercial sédentaire (en invalidité à 50%)", "comptable", "Comptable", "COMPTABLE ADMINISTRATEUR DES VENTES", "conducteur de train", "Conseil en gestion en comptabilité", "Conseiller de vente", "Conseiller en professionnalisation", "Conseillère clientèle", "Conseillère Clientèle", "Conseillere emploi", "CONSEILLERE EN INSERTION, FORMATRICE", "Consolideur, comptabilité internationale", "Consultant spécialisé art contemporain", "CONSULTANTE", "Contrôleur de gestion", "Contrôleur de gestion Industriel", "controleur de qualité et securite", "Contrôleur des impots", "cuisinière", "Développeur PHP", "Diététicienne", "DIRECTEUR GENERAL", "directrice adjointe en périscolaire", "directrice de crèche", "Docteur en PHARMACIE", "Doctorant", "DRH", "Educateur sportif", "éducatrice spécialisée", "Electricien", "employer de commerce", "Enseignant", "enseignant en activité adaptée", "ENSEIGNANT-CHERCHEUR", "enseignante", "Enseignante dans le premier degré", "fonctionnaire à temps non complet (17h30)", "Fonctionnaire secrétaire administrative", "fonctionnaire territorial", "Game Designer (Jeux vidéo)", "Gestionnaire Administratif", "Gestionnaire de flux industriel", "Gestionnaire de moyen", "gestionnaire de paie", "Gestionnaire des ressources humaines", "gestionnaire tarification", "Idem", "Infirmière", "informaticien", "ingénieur", "Ingénieur", "Ingenieur d essais dans l’industrie", "Ingénieur en bureau études structure bâtiment", "Ingénieur en informatique", "Ingénieur environnement", "Ingénieur étude et développement", "Ingénieur études", "Ingénieur Génie Industriel", "Ingénieur informatique", "Ingénieur Inserm", "Ingénieur Radio", "Ingenieur systeme", "Ingénieur télécom et informatique", "Ingenieur tri valo", "INSPECTEUR DES FINANCES", "Interne en médecine générale", "juriste", "Juriste secteur médico-social", "kinésithérapeute", "manager d’équipe de chefs de projet", "Manipulateur en électroradiologie", "manipulatrice en radiotherapie", "monitrice auto-école", "negociatrice immobilier", "Orthophoniste", "Ouvrier bâtiment", "Pharmacien", "photographe, directeur de la photographie", "professeur de maths au lycée", "PROFESSEUR DES ECOLES", "professeur des écoles", "Professeur des écoles sans spécialité", "Rédacteur juridique", "RESPONSABLE BUREAU D’ETUDE", "Responsable Commercial Bois", "Responsable comptable", "RESPONSABLE COMPTABLE", "Responsable d’ordonnancement", "Responsable de communication", "Responsable de mécénat", "Responsable marketing (sciences de la vie)", "RESPONSABLE RH", "Responsable Santé Sécurité au Travail", "Responsable Territorial", "Sage-femme", "secretaire", "SECRETAIRE", "Secrétaire Assistante/Commerciale", "Secrétaire d’établissement dans un IME", "secrétaire de rédaction", "Secrétaire-coordonnateur (gestionnaire d’association) en France", "support informatique Météo France", "TECHNICIEN ADMINISTRATIF", "Technicien atelier", "technicien de laboratoire", "technicien étude de prix électrique", "Technicien Hydrologue", "Technicien vidéo audiovisuel", "technicienne de laboratoire", "TECHNICIENNE METHODES", "TELE COMMERCIALE", "Toujours la même", "Vendeur", "Voiture pilote convois exceptionnels".
pr_sap_qual
| Variable type |
integer |
| Number of missing obs. |
301 (66.3 %) |
| Number of unique values |
9 |
| Median |
7 |
| 1st and 3rd quartiles |
6; 9 |
| Min. and max. |
1; 9 |
pr_sap_statut
| Variable type |
integer |
| Number of missing obs. |
282 (62.11 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 3 |
pr_sap_temps
| Variable type |
integer |
| Number of missing obs. |
287 (63.22 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
pr_type
- The variable only takes one (non-missing) value: "P". The variable contains 15.64 % missing observations.
id_pr_cat
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
3 |
| Mode |
“id_01_pr_03” |
- Observed factor levels: "id_01", "id_01_pr_03", "pr_03".
pr_parc_prof
| Variable type |
integer |
| Number of missing obs. |
74 (16.3 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
pr_date_parc_prof
| Variable type |
character |
| Number of missing obs. |
191 (42.07 %) |
| Number of unique values |
40 |
| Mode |
“2010-06-30” |
- Observed factor levels: "1961-06-30", "1968-06-30", "1972-06-30", "1978-06-30", "1980-06-30", "1981-06-30", "1982-06-30", "1983-06-30", "1986-06-30", "1987-06-30", "1988-06-30", "1990-06-30", "1991-06-30", "1992-06-30", "1993-06-30", "1994-06-30", "1995-06-30", "1996-06-30", "1997-06-30", "1998-06-30", "1999-06-30", "2000-06-30", "2001-06-30", "2002-06-30", "2003-06-30", "2004-06-30", "2005-06-30", "2006-06-30", "2007-06-30", "2008-06-30", "2009-06-30", "2010-06-30", "2011-06-30", "2012-06-30", "2013-06-30", "2014-06-30", "2015-06-30", "2016-06-30", "2017-06-30", "2018-06-30".
pr_age_parc_prof
| Variable type |
integer |
| Number of missing obs. |
192 (42.29 %) |
| Number of unique values |
20 |
| Median |
23 |
| 1st and 3rd quartiles |
21; 25 |
| Min. and max. |
15; 37 |
pr_statut_emploi
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
3 |
| Mode |
“Chômeurs et autres” |
- Observed factor levels: "Actifs occupés", "Chômeurs et autres", "Retraités".
pr_sap_csp_prof
| Variable type |
integer |
| Number of missing obs. |
279 (61.45 %) |
| Number of unique values |
5 |
| Median |
4 |
| 1st and 3rd quartiles |
3; 4 |
| Min. and max. |
2; 6 |
pr_int_nb
| Variable type |
integer |
| Number of missing obs. |
331 (72.91 %) |
| Number of unique values |
7 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 10 |
pr_int_date_creation
| Variable type |
character |
| Number of missing obs. |
331 (72.91 %) |
| Number of unique values |
88 |
| Mode |
“2018-12-27” |
- Observed factor levels: "2017-04-10", "2017-04-11", "2017-04-12", "2017-05-03", "2017-05-04", "2017-05-08", "2017-05-09", "2017-05-11", "2017-05-12", "2017-05-15", "2017-05-18", "2017-05-19", "2017-05-24", "2017-06-02", "2017-06-13", "2017-06-15", "2017-06-20", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-07-01", "2017-07-05", "2017-07-18", "2017-07-22", "2017-07-24", "2017-07-25", "2017-09-11", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-29", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-30", "2017-12-11", "2017-12-12", "2017-12-19", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-23", "2018-02-01", "2018-02-08", "2018-02-25", "2018-03-09", "2018-05-03", "2018-11-22", "2018-11-24", "2018-11-30", "2018-12-02", "2018-12-03", "2018-12-05", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-17", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-05", "2019-01-06", "2019-01-10", "2019-01-14", "2019-01-15", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-27", "2019-02-01", "2019-02-14", "2019-02-18", "2019-02-26", "2019-03-11", "2019-03-30".
pr_int_type
- The variable only takes one (non-missing) value: "P". The variable contains 72.91 % missing observations.
pr_int_interromp
- The variable only takes one (non-missing) value: "1". The variable contains 72.91 % missing observations.
pr_int_cat
| Variable type |
character |
| Number of missing obs. |
331 (72.91 %) |
| Number of unique values |
2 |
| Mode |
“pr_03_int_03” |
- Observed factor levels: "int_03", "pr_03_int_03".
pr_int01_an
| Variable type |
integer |
| Number of missing obs. |
339 (74.67 %) |
| Number of unique values |
26 |
| Median |
2011 |
| 1st and 3rd quartiles |
2005.5; 2015 |
| Min. and max. |
1966; 2018 |
pr_int02_an
| Variable type |
integer |
| Number of missing obs. |
413 (90.97 %) |
| Number of unique values |
17 |
| Median |
2012 |
| 1st and 3rd quartiles |
2009; 2016 |
| Min. and max. |
1992; 2018 |
pr_int03_an
| Variable type |
integer |
| Number of missing obs. |
436 (96.04 %) |
| Number of unique values |
13 |
| Median |
2013.5 |
| 1st and 3rd quartiles |
2010.25; 2016.75 |
| Min. and max. |
1994; 2018 |
pr_int04_an
| Variable type |
integer |
| Number of missing obs. |
443 (97.58 %) |
| Number of unique values |
9 |
| Median |
2014 |
| 1st and 3rd quartiles |
2010.5; 2015.5 |
| Min. and max. |
1999; 2018 |
pr_int05_an
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
4 |
| Median |
2014 |
| 1st and 3rd quartiles |
2013; 2015 |
| Min. and max. |
2011; 2016 |
pr_int06_an
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Median |
2016 |
| 1st and 3rd quartiles |
2015; 2017 |
| Min. and max. |
2014; 2018 |
pr_int07_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2016 |
| 1st and 3rd quartiles |
2015.5; 2016.5 |
| Min. and max. |
2015; 2017 |
pr_int08_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2016.5 |
| 1st and 3rd quartiles |
2016.25; 2016.75 |
| Min. and max. |
2016; 2017 |
pr_int09_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2017.5 |
| 1st and 3rd quartiles |
2017.25; 2017.75 |
| Min. and max. |
2017; 2018 |
pr_int10_an
- The variable only takes one (non-missing) value: "2018". The variable contains 99.56 % missing observations.
pr_int01_mois
| Variable type |
integer |
| Number of missing obs. |
341 (75.11 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
3; 9 |
| Min. and max. |
1; 12 |
pr_int02_mois
| Variable type |
integer |
| Number of missing obs. |
414 (91.19 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
3.75; 9.25 |
| Min. and max. |
1; 12 |
pr_int03_mois
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
10 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 10 |
| Min. and max. |
1; 12 |
pr_int04_mois
| Variable type |
integer |
| Number of missing obs. |
444 (97.8 %) |
| Number of unique values |
6 |
| Median |
7 |
| 1st and 3rd quartiles |
5.25; 8 |
| Min. and max. |
2; 11 |
pr_int05_mois
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
4 |
| Median |
10 |
| 1st and 3rd quartiles |
3; 12 |
| Min. and max. |
1; 12 |
pr_int06_mois
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Median |
6 |
| 1st and 3rd quartiles |
4.5; 8.5 |
| Min. and max. |
3; 11 |
pr_int07_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int08_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
5 |
| 1st and 3rd quartiles |
4; 6 |
| Min. and max. |
3; 7 |
pr_int09_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int10_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
6.5 |
| 1st and 3rd quartiles |
4.25; 8.75 |
| Min. and max. |
2; 11 |
pr_int01_reprise_an
| Variable type |
integer |
| Number of missing obs. |
363 (79.96 %) |
| Number of unique values |
23 |
| Median |
2013 |
| 1st and 3rd quartiles |
2008.5; 2015 |
| Min. and max. |
1992; 2018 |
pr_int02_reprise_an
| Variable type |
integer |
| Number of missing obs. |
424 (93.39 %) |
| Number of unique values |
12 |
| Median |
2014 |
| 1st and 3rd quartiles |
2010.25; 2016 |
| Min. and max. |
1992; 2018 |
pr_int03_reprise_an
| Variable type |
integer |
| Number of missing obs. |
439 (96.7 %) |
| Number of unique values |
9 |
| Median |
2014 |
| 1st and 3rd quartiles |
2011; 2015 |
| Min. and max. |
2002; 2017 |
pr_int04_reprise_an
| Variable type |
integer |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
9 |
| Median |
2013 |
| 1st and 3rd quartiles |
2010; 2015 |
| Min. and max. |
2000; 2018 |
pr_int05_reprise_an
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
3 |
| Median |
2014 |
| 1st and 3rd quartiles |
2014; 2016 |
| Min. and max. |
2011; 2016 |
pr_int06_reprise_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2015 |
| 1st and 3rd quartiles |
2014.5; 2015.5 |
| Min. and max. |
2014; 2016 |
pr_int07_reprise_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2016 |
| 1st and 3rd quartiles |
2015.5; 2016.5 |
| Min. and max. |
2015; 2017 |
pr_int08_reprise_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2016.5 |
| 1st and 3rd quartiles |
2016.25; 2016.75 |
| Min. and max. |
2016; 2017 |
pr_int09_reprise_an
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2017.5 |
| 1st and 3rd quartiles |
2017.25; 2017.75 |
| Min. and max. |
2017; 2018 |
pr_int10_reprise_an
- The variable only takes one (non-missing) value: "2018". The variable contains 99.56 % missing observations.
pr_int01_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
363 (79.96 %) |
| Number of unique values |
12 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 9 |
| Min. and max. |
1; 12 |
pr_int02_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
424 (93.39 %) |
| Number of unique values |
10 |
| Median |
5 |
| 1st and 3rd quartiles |
2.25; 9 |
| Min. and max. |
1; 12 |
pr_int03_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
438 (96.48 %) |
| Number of unique values |
8 |
| Median |
6.5 |
| 1st and 3rd quartiles |
4.5; 9 |
| Min. and max. |
1; 11 |
pr_int04_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
6 |
| Median |
9 |
| 1st and 3rd quartiles |
6.25; 9.5 |
| Min. and max. |
3; 12 |
pr_int05_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Median |
6.5 |
| 1st and 3rd quartiles |
2.5; 10.25 |
| Min. and max. |
1; 11 |
pr_int06_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
10 |
| 1st and 3rd quartiles |
9; 11 |
| Min. and max. |
8; 12 |
pr_int07_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
5 |
| 1st and 3rd quartiles |
3.5; 6.5 |
| Min. and max. |
2; 8 |
pr_int08_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
8 |
| 1st and 3rd quartiles |
7.5; 8.5 |
| Min. and max. |
7; 9 |
pr_int09_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
5.5 |
| 1st and 3rd quartiles |
3.75; 7.25 |
| Min. and max. |
2; 9 |
pr_int10_reprise_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
11.5 |
| 1st and 3rd quartiles |
11.25; 11.75 |
| Min. and max. |
11; 12 |
pr_int01_cadre
| Variable type |
integer |
| Number of missing obs. |
337 (74.23 %) |
| Number of unique values |
4 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_int02_cadre
| Variable type |
integer |
| Number of missing obs. |
415 (91.41 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_int03_cadre
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_int04_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 97.58 % missing observations.
pr_int05_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 98.68 % missing observations.
pr_int06_cadre
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 500 |
| Min. and max. |
1; 999 |
pr_int07_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int08_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int09_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int10_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int01_cadre_autre
| Variable type |
character |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Mode |
“cabinet dentaire mutualiste” |
- Observed factor levels: "cabinet dentaire mutualiste", "Chez la famille", "Garde d’enfant à mon domicile", "hopital", "la route", "Mi temps thérapeutique".
pr_int02_cadre_autre
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Mode |
“hopital” |
- Observed factor levels: "hopital", "ITEP CENTRE POUR ENFANTS", "Mi temps thérapeutique".
pr_int03_cadre_autre
- The variable only takes one (non-missing) value: "hopital". The variable contains 99.78 % missing observations.
pr_int04_cadre_autre
- The variable only takes one value: "NA".
pr_int05_cadre_autre
- The variable only takes one value: "NA".
pr_int06_cadre_autre
- The variable only takes one (non-missing) value: "mi temps". The variable contains 99.78 % missing observations.
pr_int07_cadre_autre
- The variable only takes one value: "NA".
pr_int08_cadre_autre
- The variable only takes one value: "NA".
pr_int09_cadre_autre
- The variable only takes one value: "NA".
pr_int10_cadre_autre
- The variable only takes one value: "NA".
pr_int01_contrat
| Variable type |
integer |
| Number of missing obs. |
337 (74.23 %) |
| Number of unique values |
7 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 999 |
pr_int02_contrat
| Variable type |
integer |
| Number of missing obs. |
415 (91.41 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
2; 999 |
pr_int03_contrat
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
2 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
2; 3 |
pr_int04_contrat
| Variable type |
integer |
| Number of missing obs. |
443 (97.58 %) |
| Number of unique values |
4 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
2; 999 |
pr_int05_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 98.68 % missing observations.
pr_int06_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.34 % missing observations.
pr_int07_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int08_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int09_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int10_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int01_contrat_autre
| Variable type |
character |
| Number of missing obs. |
447 (98.46 %) |
| Number of unique values |
5 |
| Mode |
“fonctionnaire” |
- Observed factor levels: "CAE", "CAE (Contrat d’Accompagnement dans l’Emploi)", "Contrat d’avenir", "CUI-CAE (en CDD)", "fonctionnaire".
pr_int02_contrat_autre
- The variable only takes one (non-missing) value: "fonctionnaire". The variable contains 99.78 % missing observations.
pr_int03_contrat_autre
- The variable only takes one value: "NA".
pr_int04_contrat_autre
- The variable only takes one (non-missing) value: "CAE (Contrat d’Accompagnement dans l’Emploi)". The variable contains 99.78 % missing observations.
pr_int05_contrat_autre
- The variable only takes one value: "NA".
pr_int06_contrat_autre
- The variable only takes one value: "NA".
pr_int07_contrat_autre
- The variable only takes one value: "NA".
pr_int08_contrat_autre
- The variable only takes one value: "NA".
pr_int09_contrat_autre
- The variable only takes one value: "NA".
pr_int10_contrat_autre
- The variable only takes one value: "NA".
pr_int01_duree
| Variable type |
integer |
| Number of missing obs. |
361 (79.52 %) |
| Number of unique values |
7 |
| Median |
7 |
| 1st and 3rd quartiles |
3; 7 |
| Min. and max. |
1; 7 |
pr_int02_duree
| Variable type |
integer |
| Number of missing obs. |
423 (93.17 %) |
| Number of unique values |
6 |
| Median |
7 |
| 1st and 3rd quartiles |
4.5; 7 |
| Min. and max. |
1; 7 |
pr_int03_duree
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
6 |
| Median |
3 |
| 1st and 3rd quartiles |
1; 7 |
| Min. and max. |
1; 7 |
pr_int04_duree
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
4 |
| Median |
2.5 |
| 1st and 3rd quartiles |
1.75; 4 |
| Min. and max. |
1; 7 |
pr_int05_duree
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
2 |
| Median |
7 |
| 1st and 3rd quartiles |
7; 7 |
| Min. and max. |
1; 7 |
pr_int06_duree
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 4 |
| Min. and max. |
1; 5 |
pr_int07_duree
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
4 |
| 1st and 3rd quartiles |
2.5; 5.5 |
| Min. and max. |
1; 7 |
pr_int08_duree
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
4 |
| 1st and 3rd quartiles |
2.5; 5.5 |
| Min. and max. |
1; 7 |
pr_int09_duree
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
4 |
| 1st and 3rd quartiles |
2.5; 5.5 |
| Min. and max. |
1; 7 |
pr_int10_duree
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
4 |
| 1st and 3rd quartiles |
2.5; 5.5 |
| Min. and max. |
1; 7 |
pr_int01_entreprise
| Variable type |
integer |
| Number of missing obs. |
338 (74.45 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int02_entreprise
| Variable type |
integer |
| Number of missing obs. |
415 (91.41 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int03_entreprise
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int04_entreprise
| Variable type |
integer |
| Number of missing obs. |
443 (97.58 %) |
| Number of unique values |
5 |
| Median |
4 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int05_entreprise
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
3 |
| Median |
3.5 |
| 1st and 3rd quartiles |
1.25; 5 |
| Min. and max. |
1; 5 |
pr_int06_entreprise
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
1.5; 3.5 |
| Min. and max. |
1; 5 |
pr_int07_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int08_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int09_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int10_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int01_prof
| Variable type |
character |
| Number of missing obs. |
336 (74.01 %) |
| Number of unique values |
114 |
| Mode |
“secrétaire” |
- Observed factor levels: "adjoins technique en crêche", "ADJOINT ADMINISTRATIF", "ADJOINT ADMINISTRATIF DE L’EDUCATION NATIONALE", "adjoint administratif en lycée", "Agent administratif", "AGENT ADMINISTRATIF", "Agent commercial spécialisé SNCF", "agent d’assistance aéroportuaire", "Agent de service hospitalier de nuit en EHPAD", "AGENT DES IMPOTS", "agent hospitalier", "agent maitrise grande distribution", "analyste risque de crédit dans une banque", "Assistant de conservateur", "assistant social du personnel", "Assistante administrative dans un lycée", "ASSISTANTE DE DIRECTION", "Assistante de Gestion", "assistante maternelle agréée", "assistante sociale", "Aucune", "Auditeur énergétique", "Auxiliaire de Vie Scolaire", "avocat", "AVS", "Cadre de santé", "Cadre RH", "caissière", "CHARGE COMMERCIAL", "CHARGE D’AFFAIRES", "chargé d’affaires", "Charge de clientèle", "Chargée de missions sur un projet informatique", "chauffeur routier", "chef de projet à l’international", "Chef de Réception", "Chercheur-doctorant en France", "chrirurgien dentiste en mutuelle", "Coiffeuse", "COIFFEUSE", "Commercial Sédentaire", "comptable", "Comptable", "conducteur de train", "Conseil en gestion en comptabilité", "Conseiller clientèle", "CONSEILLER EN GESTION DE PATRIMOINE", "Conseillère commerciale en assurance", "Consultant testing", "contrôle qualité", "Contrôleur de gestion", "Contrôleur de Gestion", "Contrôleur des impots", "Doctorant", "Educateur sportif", "éducatrice spécialisée", "Employé de commerce", "employer de commerce", "employer de libre service", "enseignante", "Etudiant", "externe en médecine", "fonctionnaire territorial", "Gestionnaire Administratif", "Gestionnaire financier", "Graphiste", "Infirmiere", "informaticien", "Ingénieur", "ingénieur commecial", "Ingenieur conseil", "Ingénieur d’études en biologie", "Ingénieur environnement", "Ingénieur informatique", "Ingénieur qualité", "Ingénieur télécom et informatique", "Inspecteur d’immeubles", "INTERIMAIRE pendant 13 ans", "Intervenant en garde d’enfants", "Journaliste", "Magasinier", "magasinier pièces de rechange auomobile", "Manipulateur en électroradiologie", "Médiatrice culturelle", "Opérateur DT DICT", "opticien lunetier", "opticienne", "ouvrier paysagiste", "ouvriere pepinieriste", "patisserie", "Preparatrice en pharmacie", "professeur des écoles", "professeure des écoles", "RESPONSABLE COMPTABLE", "Responsable d’ordonnancement", "Responsable distributeurs matériel de laboratoire", "Responsable Recherche et Développement", "Sage-femme", "secretaire", "secrétaire", "Secrétaire Assistante", "Secrétaire Commerciale", "Secrétaire comptable", "Secrétaire dans une école privée", "SECRETAIRE DOCUMENTALISTE EN IFSI", "Secrétaire fonctionnaire ministère de la défense", "secrétaire gestionnaire de laboratoire au CNRS", "serveuse", "Technicien électronique grand public", "Technicien Qualité", "technicienne de laboratoire", "Vendeur", "Vendeur disque", "Vendeur specialisé".
pr_int02_prof
| Variable type |
character |
| Number of missing obs. |
414 (91.19 %) |
| Number of unique values |
40 |
| Mode |
“adjoins technique en crêche” |
- Observed factor levels: "adjoins technique en crêche", "AGENT ADMINISTRATIF", "Agent CAF", "agent d’assistance aéroportuaire", "Aide à domicile", "assistant social du personnel", "Assistante administrative à la DDE", "Assistante de direction", "Auditeur énergétique", "Cadre de santé", "chargée de mission RH", "Chargée de projets", "chef de magasin", "Chercheur post-doctoral à l’Hôpital universitaire de Freiburg-im-Breisgau en Allemagne", "chrirurgien dentiste en mutelle", "COIFFEUSE", "Conseil en gestion en comptabilité", "CONSULTANTE", "Contrôle qualité", "Contrôleur des impots", "cuisinière", "Directrice d’exploitation transport", "Directrice de galerie d’art", "educatrice spécialisée", "enseignante", "Gestionnaire BDD", "Infirmiere", "Juriste dans le domaine de la formation professionnelle", "Préparateur de commandes chez grossiste médicamentaire", "Préparateur en pharmacie", "PRESIDENT", "professeure des écoles", "RESPONSABLE COMPTABLE", "Responsable de la communication", "secretaire", "Secrétaire administrative", "SECRETAIRE ADMINSITRATIVE INSTITUT REEDUCATION POUR ENFANT", "Secrétaire comptable", "Secrétaire fonctionnaire ministère de la défense", "technicienne de laboratoire".
pr_int03_prof
| Variable type |
character |
| Number of missing obs. |
436 (96.04 %) |
| Number of unique values |
18 |
| Mode |
“Assistante administrative dans une banque” |
- Observed factor levels: "Assistante administrative dans une banque", "chargée de mission RH", "chef de magasin", "Commercial", "contrôle qualité", "Controleur comptable", "Contrôleur des impots", "Employé libre service en grande surface", "enseignante", "Fonctionnaire Adjoint administratif", "Gestionnaire BDD", "Juriste secteur médico-social", "PRESIDENT", "RESPONSABLE COMPTABLE", "secretaire", "Secrétaire a la médecine du travail de l’UBO", "SECRETAIRE ADMINISTRATIVE SOCIETE NETTOYAGE INDUSTRIEL", "technicienne de laboratoire".
pr_int04_prof
| Variable type |
character |
| Number of missing obs. |
443 (97.58 %) |
| Number of unique values |
11 |
| Mode |
“Adjoint administratif à la Cavale Blanche” |
- Observed factor levels: "Adjoint administratif à la Cavale Blanche", "Assistante administrative dans une école primaire", "ASSITANTE DE DIRECTION INSTITUT REEDUCATION POUR ENFANTS", "billetterie aerienne informatisée", "Chargée de missions (gestion, informatique puis ressources humaines)", "Commercial -métreur", "contrôle qualité", "Contrôleur des impots", "Fontionnaire Secrétaire administrative", "RESPONSABLE COMPTABLE", "secretaire".
pr_int05_prof
| Variable type |
character |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Mode |
“agent comptoir location voiture” |
- Observed factor levels: "agent comptoir location voiture", "Auditeur énergétique", "contrôle qualité", "Contrôleur des impots", "Fonctionnaire Secrétaire administrative", "secretaire".
pr_int06_prof
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Mode |
“agent comptoir location voiture” |
- Observed factor levels: "agent comptoir location voiture", "Contrôleur des impots", "secretaire".
pr_int07_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int08_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int09_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int10_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int01_qual
| Variable type |
integer |
| Number of missing obs. |
342 (75.33 %) |
| Number of unique values |
8 |
| Median |
7 |
| 1st and 3rd quartiles |
6; 9 |
| Min. and max. |
1; 9 |
pr_int02_qual
| Variable type |
integer |
| Number of missing obs. |
416 (91.63 %) |
| Number of unique values |
9 |
| Median |
7 |
| 1st and 3rd quartiles |
6; 9 |
| Min. and max. |
1; 9 |
pr_int03_qual
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
8 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
2; 9 |
pr_int04_qual
| Variable type |
integer |
| Number of missing obs. |
443 (97.58 %) |
| Number of unique values |
4 |
| Median |
9 |
| 1st and 3rd quartiles |
6.5; 9 |
| Min. and max. |
2; 9 |
pr_int05_qual
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
5 |
| Median |
6 |
| 1st and 3rd quartiles |
5.25; 6.75 |
| Min. and max. |
2; 9 |
pr_int06_qual
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
2 |
| Median |
9 |
| 1st and 3rd quartiles |
7.5; 9 |
| Min. and max. |
6; 9 |
pr_int07_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int08_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int09_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int10_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int01_raisons_1
- The variable only takes one (non-missing) value: "1". The variable contains 97.36 % missing observations.
pr_int02_raisons_1
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
pr_int03_raisons_1
- The variable only takes one value: "NA".
pr_int04_raisons_1
- The variable only takes one value: "NA".
pr_int05_raisons_1
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int06_raisons_1
- The variable only takes one value: "NA".
pr_int07_raisons_1
- The variable only takes one value: "NA".
pr_int08_raisons_1
- The variable only takes one value: "NA".
pr_int09_raisons_1
- The variable only takes one value: "NA".
pr_int10_raisons_1
- The variable only takes one value: "NA".
pr_int01_raisons_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
pr_int02_raisons_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int03_raisons_2
- The variable only takes one value: "NA".
pr_int04_raisons_2
- The variable only takes one value: "NA".
pr_int05_raisons_2
- The variable only takes one value: "NA".
pr_int06_raisons_2
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int07_raisons_2
- The variable only takes one value: "NA".
pr_int08_raisons_2
- The variable only takes one value: "NA".
pr_int09_raisons_2
- The variable only takes one value: "NA".
pr_int10_raisons_2
- The variable only takes one value: "NA".
pr_int01_raisons_3
- The variable only takes one (non-missing) value: "1". The variable contains 98.68 % missing observations.
pr_int02_raisons_3
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int03_raisons_3
- The variable only takes one value: "NA".
pr_int04_raisons_3
- The variable only takes one value: "NA".
pr_int05_raisons_3
- The variable only takes one value: "NA".
pr_int06_raisons_3
- The variable only takes one value: "NA".
pr_int07_raisons_3
- The variable only takes one value: "NA".
pr_int08_raisons_3
- The variable only takes one value: "NA".
pr_int09_raisons_3
- The variable only takes one value: "NA".
pr_int10_raisons_3
- The variable only takes one value: "NA".
pr_int01_raisons_4
- The variable only takes one (non-missing) value: "1". The variable contains 96.92 % missing observations.
pr_int02_raisons_4
- The variable only takes one (non-missing) value: "1". The variable contains 98.9 % missing observations.
pr_int03_raisons_4
- The variable only takes one (non-missing) value: "1". The variable contains 98.9 % missing observations.
pr_int04_raisons_4
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
pr_int05_raisons_4
- The variable only takes one value: "NA".
pr_int06_raisons_4
- The variable only takes one value: "NA".
pr_int07_raisons_4
- The variable only takes one value: "NA".
pr_int08_raisons_4
- The variable only takes one value: "NA".
pr_int09_raisons_4
- The variable only takes one value: "NA".
pr_int10_raisons_4
- The variable only takes one value: "NA".
pr_int01_raisons_5
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
pr_int02_raisons_5
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int03_raisons_5
- The variable only takes one value: "NA".
pr_int04_raisons_5
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int05_raisons_5
- The variable only takes one value: "NA".
pr_int06_raisons_5
- The variable only takes one value: "NA".
pr_int07_raisons_5
- The variable only takes one value: "NA".
pr_int08_raisons_5
- The variable only takes one value: "NA".
pr_int09_raisons_5
- The variable only takes one value: "NA".
pr_int10_raisons_5
- The variable only takes one value: "NA".
pr_int01_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 88.77 % missing observations.
pr_int02_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 95.37 % missing observations.
pr_int03_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 98.68 % missing observations.
pr_int04_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
pr_int05_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int06_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int07_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int08_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int09_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int10_raisons_6
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int01_raisons_7
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
pr_int02_raisons_7
- The variable only takes one (non-missing) value: "1". The variable contains 99.12 % missing observations.
pr_int03_raisons_7
- The variable only takes one value: "NA".
pr_int04_raisons_7
- The variable only takes one value: "NA".
pr_int05_raisons_7
- The variable only takes one value: "NA".
pr_int06_raisons_7
- The variable only takes one value: "NA".
pr_int07_raisons_7
- The variable only takes one value: "NA".
pr_int08_raisons_7
- The variable only takes one value: "NA".
pr_int09_raisons_7
- The variable only takes one value: "NA".
pr_int10_raisons_7
- The variable only takes one value: "NA".
pr_int01_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 90.31 % missing observations.
pr_int02_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 96.92 % missing observations.
pr_int03_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 98.24 % missing observations.
pr_int04_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 98.9 % missing observations.
pr_int05_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.34 % missing observations.
pr_int06_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int07_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int08_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int09_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int10_raisons_999
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
pr_int01_raisons_autre
| Variable type |
character |
| Number of missing obs. |
410 (90.31 %) |
| Number of unique values |
39 |
| Mode |
“accident de travail” |
- Observed factor levels: "1 ere grossesse arret de 9 mois au total", "accident de travail", "arrêt liée à une grossesse", "arret maladie pour sur-infection", "Asthénie, crise d’angoisse", "au moment du diagnostic de la muco", "Bilan pré greffe", "Burn out + infection + dépression", "Burnout", "Congés maternité puis parental", "cure antibiotique", "Cure antibiotiques par intraveineuse", "Décès familiale", "dégradation de l’état de santé", "DEMENGAMENT", "En attente de greffe pulmonaire", "État critique nécessitant une greffe", "exacerbation de la maladie pendant ma grossesse", "FIN DE CONTRAT POUR CAUSE DE FATIGUE LIE A LA MUCOVISCIDOSE", "greffe pulmonaire", "Greffe pulmonaire", "grossesse", "Grossesse", "Grossesse + post grossesse", "harcèlement moral", "harcèlement moral dépression", "Hospitalisation", "Inscription sur liste d’attente pour greffe pulmonaire", "LONGUE CURE IV EPROUVANTE", "Maladie", "Occlusion intestinale", "Pancréatite - hospitalisation d’1 semaine", "perfusions d’antibiotiques prévues pour 3 mois", "Reprise d’études", "reprise d’études par correspondance psychologie", "Reprise des études (bac +3)", "rupture conventionnelle", "Rupture conventionnelle", "rupture conventionnelle + exacerbation liée à la maladie".
pr_int02_raisons_autre
| Variable type |
character |
| Number of missing obs. |
440 (96.92 %) |
| Number of unique values |
14 |
| Mode |
“2 eme grossesse” |
- Observed factor levels: "2 eme grossesse", "arret maladie pour sur-infection", "cure antibiotique", "dégradation de la fonction respiratoire puis occlusion intestinale", "Dépression rédactionnelle consecutive à une problématique professionnelle. Reconnaissance de maladie professionnelle", "GROSSESSE", "Grossesse pathologique", "inapte au poste", "maladie", "MISE SOUS ORKAMBIE", "Opération sinus", "Passage en licence 3 par alternance", "Rupture conventionnelle de CDD (suite à une phase dépressive )", "syndrôme grippal".
pr_int03_raisons_autre
| Variable type |
character |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
8 |
| Mode |
“Arrêt maladie de plus d?un mois opération sinus, occlusion…..” |
- Observed factor levels: "Arrêt maladie de plus d?un mois opération sinus, occlusion…..", "arret maladie pour sur-infection", "Changement d’entreprise", "cure antibiotique", "inaptitude medecine du travail", "Maladie", "RUPTURE CONVENTIONNELLE", "surinfection, dispnée".
pr_int04_raisons_autre
| Variable type |
character |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
5 |
| Mode |
“cure antibiotique” |
- Observed factor levels: "cure antibiotique", "Grossesse", "Maladie", "Nouvelle opportunité professionnelle", "Opération sinus".
pr_int05_raisons_autre
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Mode |
“cure antibiotique” |
- Observed factor levels: "cure antibiotique", "Maladie", "Opération".
pr_int06_raisons_autre
- The variable only takes one (non-missing) value: "cure antibiotique". The variable contains 99.78 % missing observations.
pr_int07_raisons_autre
- The variable only takes one (non-missing) value: "cure antibiotique". The variable contains 99.78 % missing observations.
pr_int08_raisons_autre
- The variable only takes one (non-missing) value: "cure antibiotique". The variable contains 99.78 % missing observations.
pr_int09_raisons_autre
- The variable only takes one (non-missing) value: "cure antibiotique". The variable contains 99.78 % missing observations.
pr_int10_raisons_autre
- The variable only takes one (non-missing) value: "cure antibiotique". The variable contains 99.78 % missing observations.
pr_int01_statut
| Variable type |
integer |
| Number of missing obs. |
335 (73.79 %) |
| Number of unique values |
2 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 3 |
pr_int02_statut
| Variable type |
integer |
| Number of missing obs. |
413 (90.97 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 3 |
pr_int03_statut
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
2 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
2; 3 |
pr_int04_statut
- The variable only takes one (non-missing) value: "3". The variable contains 97.58 % missing observations.
pr_int05_statut
- The variable only takes one (non-missing) value: "3". The variable contains 98.68 % missing observations.
pr_int06_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.34 % missing observations.
pr_int07_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int08_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int09_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int10_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int01_non_repr
| Variable type |
integer |
| Number of missing obs. |
441 (97.14 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 3 |
| Min. and max. |
1; 3 |
pr_int02_non_repr
| Variable type |
integer |
| Number of missing obs. |
447 (98.46 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 3 |
| Min. and max. |
1; 3 |
pr_int03_non_repr
- The variable only takes one value: "NA".
pr_int04_non_repr
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
2 |
| 1st and 3rd quartiles |
1.5; 2.5 |
| Min. and max. |
1; 3 |
pr_int05_non_repr
- The variable only takes one value: "NA".
pr_int06_non_repr
- The variable only takes one (non-missing) value: "3". The variable contains 99.78 % missing observations.
pr_int07_non_repr
- The variable only takes one value: "NA".
pr_int08_non_repr
- The variable only takes one value: "NA".
pr_int09_non_repr
- The variable only takes one value: "NA".
pr_int10_non_repr
- The variable only takes one value: "NA".
pr_int01_reprise
| Variable type |
integer |
| Number of missing obs. |
331 (72.91 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
pr_int02_reprise
| Variable type |
integer |
| Number of missing obs. |
412 (90.75 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
pr_int03_reprise
| Variable type |
integer |
| Number of missing obs. |
436 (96.04 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
pr_int04_reprise
| Variable type |
integer |
| Number of missing obs. |
443 (97.58 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
pr_int05_reprise
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
pr_int06_reprise
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0.5; 1 |
| Min. and max. |
0; 1 |
pr_int07_reprise
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int08_reprise
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int09_reprise
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int10_reprise
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int01_reprise_cadre
| Variable type |
integer |
| Number of missing obs. |
365 (80.4 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_int02_reprise_cadre
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_int03_reprise_cadre
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 999 |
pr_int04_reprise_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 98.02 % missing observations.
pr_int05_reprise_cadre
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 999 |
| Min. and max. |
1; 999 |
pr_int06_reprise_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int07_reprise_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int08_reprise_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int09_reprise_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int10_reprise_cadre
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
pr_int01_reprise_cadre_autre
| Variable type |
character |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Mode |
“foyer pour handicapés puis centre pour enfants” |
- Observed factor levels: "foyer pour handicapés puis centre pour enfants", "hopital", "la route", "Mi temps thérapeutique".
pr_int02_reprise_cadre_autre
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“hopital” |
- Observed factor levels: "hopital", "Mi temps thérapeutique".
pr_int03_reprise_cadre_autre
- The variable only takes one (non-missing) value: "hopital". The variable contains 99.78 % missing observations.
pr_int04_reprise_cadre_autre
- The variable only takes one value: "NA".
pr_int05_reprise_cadre_autre
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Mi temps ammenagé / invalidité cat 1” |
- Observed factor levels: "Mi temps ammenagé / invalidité cat 1", "Mi temps thérapeutique".
pr_int06_reprise_cadre_autre
- The variable only takes one value: "NA".
pr_int07_reprise_cadre_autre
- The variable only takes one value: "NA".
pr_int08_reprise_cadre_autre
- The variable only takes one value: "NA".
pr_int09_reprise_cadre_autre
- The variable only takes one value: "NA".
pr_int10_reprise_cadre_autre
- The variable only takes one value: "NA".
pr_int01_reprise_contrat
| Variable type |
integer |
| Number of missing obs. |
361 (79.52 %) |
| Number of unique values |
6 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 3 |
| Min. and max. |
1; 999 |
pr_int02_reprise_contrat
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
4 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
2; 999 |
pr_int03_reprise_contrat
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
4 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 999 |
pr_int04_reprise_contrat
| Variable type |
integer |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
2 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
3; 999 |
pr_int05_reprise_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 98.9 % missing observations.
pr_int06_reprise_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int07_reprise_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int08_reprise_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int09_reprise_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int10_reprise_contrat
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int01_reprise_contrat_autre
| Variable type |
character |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
2 |
| Mode |
“fonctionnaire” |
- Observed factor levels: "CUI-CAE (en CDD)", "fonctionnaire".
pr_int02_reprise_contrat_autre
- The variable only takes one (non-missing) value: "fonctionnaire". The variable contains 99.78 % missing observations.
pr_int03_reprise_contrat_autre
- The variable only takes one (non-missing) value: "CAE (Contrat d’Accompagnement dans l’Emploi)". The variable contains 99.78 % missing observations.
pr_int04_reprise_contrat_autre
- The variable only takes one (non-missing) value: "CUI (Contrat Unique d’Insertion)". The variable contains 99.78 % missing observations.
pr_int05_reprise_contrat_autre
- The variable only takes one value: "NA".
pr_int06_reprise_contrat_autre
- The variable only takes one value: "NA".
pr_int07_reprise_contrat_autre
- The variable only takes one value: "NA".
pr_int08_reprise_contrat_autre
- The variable only takes one value: "NA".
pr_int09_reprise_contrat_autre
- The variable only takes one value: "NA".
pr_int10_reprise_contrat_autre
- The variable only takes one value: "NA".
pr_int01_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
362 (79.74 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int02_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int03_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
5 |
| Median |
4 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
pr_int04_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
4 |
| Median |
2.5 |
| 1st and 3rd quartiles |
1.75; 5 |
| Min. and max. |
1; 5 |
pr_int05_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 5 |
| Min. and max. |
1; 5 |
pr_int06_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int07_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int08_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int09_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int10_reprise_entreprise
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
1.5 |
| 1st and 3rd quartiles |
1.25; 1.75 |
| Min. and max. |
1; 2 |
pr_int01_reprise_prof
| Variable type |
character |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Mode |
“Assistante administrative à la DDE” |
- Observed factor levels: "Assistante administrative à la DDE", "Auditeur énergétique", "Chercheur post-doctoral à l’Hôpital universitaire de Freiburg-im-Breisgau en Allemagne", "patisserie", "professeure des écoles", "reprise au même poste".
pr_int02_reprise_prof
| Variable type |
character |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
29 |
| Mode |
“agent d’assistance aéroportuaire” |
- Observed factor levels: "agent d’assistance aéroportuaire", "assistant social du personnel", "Assistante administrative dans une banque", "ASSISTANTE ADMINISTRATIVE SOCIETE NETTOYAGE INDUSTRIEL", "Auditeur énergétique", "Barman", "CHARGE CONTENTIEUX LOCATIF", "chargée de mission RH", "Chargée de projets", "Chargée de relations publiques pour un salon", "chef de magasin", "Conseil en gestion en comptabilité", "CONSULTANTE", "contrôle qualité", "Controleur Comptable", "Contrôleur des impots", "cuisisnière", "Employer libre service de grande surface", "enseignante", "Gestionnaire BDD", "Juriste secteur médico-social", "PRESIDENT", "RESPONSABLE COMPTABLE", "Responsable de mécénat", "RESPONSABLE RH", "secretaire", "Secrétaire", "Secrétaire administrative", "technicienne de laboratoire".
pr_int03_reprise_prof
| Variable type |
character |
| Number of missing obs. |
438 (96.48 %) |
| Number of unique values |
15 |
| Mode |
“Adjoint administratif” |
- Observed factor levels: "Adjoint administratif", "agent de voyages", "Assistante administrative dans une école primaire", "ASSISTANTE DE DIRECTION INSTITUT REEDUCATION ENFANT", "Chargée de missions (gestion, informatique puis ressources humaines)", "Comptable", "contrôle qualité", "Contrôleur des impots", "DIRECTEUR GENERAL", "enseignante", "Gestionnaire BDD", "RESPONSABLE COMPTABLE", "secretaire", "Technicien Hydrologue", "technicienne de laboratoire".
pr_int04_reprise_prof
| Variable type |
character |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
9 |
| Mode |
“agent location voitures” |
- Observed factor levels: "agent location voitures", "Commercial métreur", "contrôle qualité", "Contrôleur des impots", "Juriste secteur médico-social", "RESPONSABLE COMPTABLE", "secretaire", "Secrétaire administrative", "Secrétaire d’établissement dans un IME".
pr_int05_reprise_prof
| Variable type |
character |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
5 |
| Mode |
“agent comptoir location voiture” |
- Observed factor levels: "agent comptoir location voiture", "Auditeur énergétique", "Contrôleur des impots", "Fonctionnaire secrétaire administrative", "secretaire".
pr_int06_reprise_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int07_reprise_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int08_reprise_prof
- The variable only takes one (non-missing) value: "secretaire". The variable contains 99.78 % missing observations.
pr_int09_reprise_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int10_reprise_prof
| Variable type |
character |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Mode |
“Contrôleur des impots” |
- Observed factor levels: "Contrôleur des impots", "secretaire".
pr_int01_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
364 (80.18 %) |
| Number of unique values |
9 |
| Median |
7 |
| 1st and 3rd quartiles |
5; 9 |
| Min. and max. |
1; 9 |
pr_int02_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
8 |
| Median |
7 |
| 1st and 3rd quartiles |
5; 9 |
| Min. and max. |
2; 9 |
pr_int03_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
8 |
| Median |
8 |
| 1st and 3rd quartiles |
5; 9 |
| Min. and max. |
2; 9 |
pr_int04_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
4 |
| Median |
9 |
| 1st and 3rd quartiles |
7; 9 |
| Min. and max. |
2; 9 |
pr_int05_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
4 |
| Median |
6 |
| 1st and 3rd quartiles |
6; 7 |
| Min. and max. |
5; 9 |
pr_int06_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int07_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int08_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int09_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int10_reprise_qual
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
7.5 |
| 1st and 3rd quartiles |
6.75; 8.25 |
| Min. and max. |
6; 9 |
pr_int01_reprise_statut
| Variable type |
integer |
| Number of missing obs. |
355 (78.19 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 3 |
pr_int02_reprise_statut
| Variable type |
integer |
| Number of missing obs. |
424 (93.39 %) |
| Number of unique values |
3 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 3 |
pr_int03_reprise_statut
| Variable type |
integer |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
2 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
2; 3 |
pr_int04_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 98.02 % missing observations.
pr_int05_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 98.9 % missing observations.
pr_int06_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int07_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int08_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int09_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int10_reprise_statut
- The variable only takes one (non-missing) value: "3". The variable contains 99.56 % missing observations.
pr_int01_duree_j
| Variable type |
numeric |
| Number of missing obs. |
361 (79.52 %) |
| Number of unique values |
7 |
| Median |
106.54 |
| 1st and 3rd quartiles |
53.27; 106.54 |
| Min. and max. |
30.44; 106.54 |
pr_int02_duree_j
| Variable type |
numeric |
| Number of missing obs. |
423 (93.17 %) |
| Number of unique values |
6 |
| Median |
106.54 |
| 1st and 3rd quartiles |
76.1; 106.54 |
| Min. and max. |
30.44; 106.54 |
pr_int03_duree_j
| Variable type |
numeric |
| Number of missing obs. |
437 (96.26 %) |
| Number of unique values |
6 |
| Median |
53.27 |
| 1st and 3rd quartiles |
30.44; 106.54 |
| Min. and max. |
30.44; 106.54 |
pr_int04_duree_j
| Variable type |
numeric |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
4 |
| Median |
45.66 |
| 1st and 3rd quartiles |
36.15; 66.59 |
| Min. and max. |
30.44; 106.54 |
pr_int05_duree_j
| Variable type |
numeric |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
2 |
| Median |
106.54 |
| 1st and 3rd quartiles |
106.54; 106.54 |
| Min. and max. |
30.44; 106.54 |
pr_int06_duree_j
| Variable type |
numeric |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
57.07 |
| 1st and 3rd quartiles |
43.76; 70.39 |
| Min. and max. |
30.44; 83.71 |
pr_int07_duree_j
| Variable type |
numeric |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
68.49 |
| 1st and 3rd quartiles |
49.47; 87.52 |
| Min. and max. |
30.44; 106.54 |
pr_int08_duree_j
| Variable type |
numeric |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
68.49 |
| 1st and 3rd quartiles |
49.47; 87.52 |
| Min. and max. |
30.44; 106.54 |
pr_int09_duree_j
| Variable type |
numeric |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
68.49 |
| 1st and 3rd quartiles |
49.47; 87.52 |
| Min. and max. |
30.44; 106.54 |
pr_int10_duree_j
| Variable type |
numeric |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
68.49 |
| 1st and 3rd quartiles |
49.47; 87.52 |
| Min. and max. |
30.44; 106.54 |
fa_date_creation
| Variable type |
character |
| Number of missing obs. |
81 (17.84 %) |
| Number of unique values |
188 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-07", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-13", "2017-04-19", "2017-04-20", "2017-04-22", "2017-04-25", "2017-04-26", "2017-04-27", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-15", "2017-05-16", "2017-05-17", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-22", "2017-06-01", "2017-06-02", "2017-06-13", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-22", "2017-06-23", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-24", "2017-07-25", "2017-07-27", "2017-08-01", "2017-08-03", "2017-08-08", "2017-08-10", "2017-08-17", "2017-08-20", "2017-08-30", "2017-09-02", "2017-09-06", "2017-09-07", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-28", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-21", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-23", "2018-01-27", "2018-01-30", "2018-01-31", "2018-02-01", "2018-02-04", "2018-02-08", "2018-02-11", "2018-02-12", "2018-02-14", "2018-02-16", "2018-02-20", "2018-02-25", "2018-02-26", "2018-03-09", "2018-03-11", "2018-03-12", "2018-03-28", "2018-04-16", "2018-04-29", "2018-05-03", "2018-05-04", "2018-05-13", "2018-05-16", "2018-05-31", "2018-06-26", "2018-07-26", "2018-07-30", "2018-08-09", "2018-09-04", "2018-09-06", "2018-09-11", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-26", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-20", "2018-12-21", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-07", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-15", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-21", "2019-01-22", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-14", "2019-02-17", "2019-02-18", "2019-02-26", "2019-02-27", "2019-03-30".
fa_fo_conjt_prof
| Variable type |
integer |
| Number of missing obs. |
326 (71.81 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
fa_fo_conjt_ress
| Variable type |
integer |
| Number of missing obs. |
326 (71.81 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 88 |
fa_fo_membres_prof
- The variable only takes one (non-missing) value: "3". The variable contains 99.78 % missing observations.
fa_fo_membres_ress
- The variable only takes one (non-missing) value: "2". The variable contains 99.78 % missing observations.
fa_fo_mere_prof
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_fo_parent_prof
| Variable type |
integer |
| Number of missing obs. |
434 (95.59 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
fa_fo_parent_ress
| Variable type |
integer |
| Number of missing obs. |
434 (95.59 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 88 |
fa_fo_pere_prof
| Variable type |
integer |
| Number of missing obs. |
424 (93.39 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
fa_fo_revenus
| Variable type |
integer |
| Number of missing obs. |
180 (39.65 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
fa_fo_revenus_an
| Variable type |
integer |
| Number of missing obs. |
407 (89.65 %) |
| Number of unique values |
13 |
| Median |
9 |
| 1st and 3rd quartiles |
7; 11 |
| Min. and max. |
1; 88 |
fa_fo_revenus_mois
| Variable type |
integer |
| Number of missing obs. |
228 (50.22 %) |
| Number of unique values |
14 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 99 |
fa_fo_vie
| Variable type |
integer |
| Number of missing obs. |
217 (47.8 %) |
| Number of unique values |
7 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 999 |
fa_fo_vie_autre
| Variable type |
character |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
8 |
| Mode |
“AVEC MA MERE ET MON BEAU PERE ET MON DEMI FRERE ET MA DEMI SOEUR” |
- Observed factor levels: "AVEC MA MERE ET MON BEAU PERE ET MON DEMI FRERE ET MA DEMI SOEUR", "Colocation", "Colocation la semaine/Chez mes deux parents le week-end", "En colocation", "en colocation avec des amis", "en concubinage", "Frère", "PACSE".
fa_fo_vos_parents_ress
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
4 |
| Median |
2 |
| 1st and 3rd quartiles |
2; 2 |
| Min. and max. |
1; 88 |
fa_fo_vous_ress
| Variable type |
integer |
| Number of missing obs. |
123 (27.09 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 88 |
fa_fratrie
| Variable type |
integer |
| Number of missing obs. |
109 (24.01 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
fa_pm_union_int_an
| Variable type |
integer |
| Number of missing obs. |
369 (81.28 %) |
| Number of unique values |
33 |
| Median |
2004 |
| 1st and 3rd quartiles |
1998; 2010 |
| Min. and max. |
1975; 2017 |
fa_pm_union_int_mois
| Variable type |
integer |
| Number of missing obs. |
382 (84.14 %) |
| Number of unique values |
12 |
| Median |
6.5 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
fa_pm_union_int_par
| Variable type |
integer |
| Number of missing obs. |
351 (77.31 %) |
| Number of unique values |
4 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 99 |
fa_mere_an_deces
| Variable type |
integer |
| Number of missing obs. |
434 (95.59 %) |
| Number of unique values |
15 |
| Median |
2012.5 |
| 1st and 3rd quartiles |
2000; 2016 |
| Min. and max. |
1984; 2018 |
fa_mere_an_nais
| Variable type |
integer |
| Number of missing obs. |
118 (25.99 %) |
| Number of unique values |
50 |
| Median |
1959 |
| 1st and 3rd quartiles |
1952; 1964 |
| Min. and max. |
1921; 1978 |
fa_mere_decede
| Variable type |
integer |
| Number of missing obs. |
112 (24.67 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_mere_entreprise
| Variable type |
integer |
| Number of missing obs. |
209 (46.04 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 5 |
| Min. and max. |
1; 5 |
fa_mere_etudes
| Variable type |
integer |
| Number of missing obs. |
119 (26.21 %) |
| Number of unique values |
12 |
| Median |
8 |
| 1st and 3rd quartiles |
4; 12 |
| Min. and max. |
1; 99 |
fa_mere_mois_deces
| Variable type |
integer |
| Number of missing obs. |
434 (95.59 %) |
| Number of unique values |
9 |
| Median |
7 |
| 1st and 3rd quartiles |
5.75; 10 |
| Min. and max. |
1; 11 |
fa_mere_mois_nais
| Variable type |
integer |
| Number of missing obs. |
114 (25.11 %) |
| Number of unique values |
12 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 9 |
| Min. and max. |
1; 12 |
fa_mere_prenom
| Variable type |
character |
| Number of missing obs. |
114 (25.11 %) |
| Number of unique values |
127 |
| Mode |
“MA” |
- Observed factor levels: "ag", "AG", "AM", "an", "An", "AN", "ar", "AR", "Au", "AU", "BA", "bc", "BE", "br", "Br", "BR", "Ca", "CA", "ce", "CE", "ch", "Ch", "CH", "cl", "Cl", "CL", "Co", "CO", "De", "DE", "Do", "DO", "ED", "el", "El", "EL", "es", "Ev", "EV", "Fa", "FA", "FL", "fr", "FR", "Ge", "GE", "GH", "gi", "Gi", "GL", "go", "Ha", "HA", "He", "HE", "HU", "is", "Is", "IS", "ja", "JA", "je", "jo", "Jo", "JO", "ka", "KA", "la", "La", "LA", "LI", "LO", "LU", "LY", "ma", "Ma", "MA", "MC", "Mi", "MI", "mo", "Mo", "MO", "MU", "My", "MY", "na", "Na", "Ne", "NE", "ni", "Ni", "NI", "od", "OD", "pa", "Pa", "PA", "Pe", "PE", "RE", "Sa", "SA", "SE", "SI", "so", "So", "SO", "ST", "SU", "sy", "Sy", "SY", "th", "TH", "va", "Va", "VA", "ve", "Ve", "VE", "Vé", "vi", "VI", "yv", "YV", "ZO".
fa_mere_prof
| Variable type |
character |
| Number of missing obs. |
137 (30.18 %) |
| Number of unique values |
245 |
| Mode |
“Secrétaire” |
- Observed factor levels: "Agent commercial en immobilier", "agent d’entretien", "AGENT D’ESCAL", "Agent de maitrise", "agent de nettoyage", "Agent des impots", "Agent multitâche en piscine", "Agent SNCF", "agent territorial", "Agente des douanes", "agricultrice", "Agricultrice", "agricultrice collaboratrice", "aide à domicile", "Aide à la personne", "aide comptable", "Aide comptable", "Aide Mécido Psychologique", "Aide Médico Psychologique", "Aide médicopsychologique en centre pour handicapés", "aide soignante", "Aide soignante", "AIDE SOIGNANTE", "aide-soignante", "Aide-soignante", "ANIMATRICE EN MEDIATHEQUE", "Animatrice en yoga du rire", "ARC", "Architect", "Architecte", "ash", "Ash", "ASH", "ASSEM", "Assistance RH", "Assistante", "Assistante administratif", "Assistante commerciale", "assistante de direction", "Assistante de direction", "ASSISTANTE DIRECTION", "Assistante familliale", "assistante maternelle", "Assistante maternelle", "ASSISTANTE MATERNELLE", "Assistante spécialisé vétérinaire", "assistante vétérinaire", "ASSITANTE DE DIRECTION", "Atsem", "ATSEM", "ATTACHEE DE PRESSE", "au foyer", "aucune", "Aucune", "AUCUNE", "Auxiliaire de puériculture", "Auxiliaire de vie", "Auxilliaire de puéricultrice", "auxilliaire de vie social", "AVS", "Banquiere", "bibliothecaire", "boulangere", "Boulangère", "Cadre", "Cadre Administratif", "Cadre de direction", "Cadre infirmière", "Cadre Telecom", "CAISSIERE", "caissière", "Caissière principale", "cantiniére", "CDI Recipharm", "CESF", "Chargé de communication", "Chargé de suivi qualité", "Chargée d’achat", "chef d’entreprise", "CHEF D’ENTREPRISE", "Chef d’équipe en restauration", "Coiffeuse", "commercante", "Commerçante", "commerciale", "Commerciale", "comptable", "Comptable", "conditionneuse", "CONSEILLER EDUCATION", "Conseillère bancaire", "Conseillère Leader Référente", "Conseillère téléphonique à la poste", "contremaitresse", "Contrôleuse qualité", "couturiére", "couturière", "Cuisinière", "dermatologue", "DG", "diététicienne", "Directeur de conservatoire", "Directrice adjointe", "Directrice administratif et financier", "Directrice administrative", "Directrice du Baccalauréat Académie Aix-Marseille", "DOCTEUR EN MEDECINE", "Educatrice spécialisée", "EMPLOYE", "employe cpam", "employé d’usine", "employé dans la restauration collective", "employé de mairie", "Employé Pôle Emploi", "employée administrative", "Employée dans une mutuelle", "EMPLOYEE DE BANQUE", "employée de banque", "Employée de banque", "employée de bureau", "Employée de bureau", "EMPLOYEE MAGASIN GMS", "employée sécurité social", "employer de magasin", "Encadrante socio-professionnelle", "Enquetrice", "Enseignant chercheur", "enseignante", "Enseignante", "Enseignante chercheuse", "Esthéticienne", "Femme de menage", "FEMME DE MENAGE", "Femme de ménage", "Fonctionnaire", "Fonctionnaire ministère des finances", "Gardienne", "gardienne dans une RPA", "gestionnaire", "horticultrice", "hotesse", "HOTESSE DE CAISSE", "HYPNOTHERAPEUTE", "INFIRMIERE", "infirmière", "Infirmière", "Infirmière scolaire", "INSPECTRICE DES FINANCES", "institutrice", "Institutrice", "Interim", "Jamais travaillé", "kiné", "LABORANTINE", "manager", "manip radio", "Manipulatrice en électroradiologie médicale", "Mécanicienne en confection dans la lingerie + femme de ménage", "Médecin", "médecin généraliste", "Médecin généraliste", "Mère au foyer", "MERE AU FOYER", "Mère au foyer", "mère aux foyers", "MILITAIRE", "modiste", "Moniteur de gestion", "Monteuse de film scientifique au CNRS", "Monteuse Vendeuse en optique", "Nounou", "Nourice", "Nourrice", "nourrice et femme de ménage", "Opérateur logistique", "Opératrice de saisie", "Ostréicultrice", "PEDICURE PODOLOGUE", "PERCEPTRICE POUR L HOPITAL", "pharmacien", "professeur", "Professeur", "professeur de mathématiques", "professeur des ecoles", "Professeur des écoles", "Professeure", "Professeure des écoles", "Psychologue", "PSYCHOMOTRICIENNE", "Puericultrice", "puéricultrice", "Puéricultrice", "Responsable adjointe hospitalière", "responsable d’exploitation", "responsable des paies", "Responsable magasin de vêtements", "Restauratrice", "Retraité de l’éducation nationale", "RRH", "Sage-femme", "Salarié", "sans", "SANS", "sans profession", "Sans profession", "Sans Profession", "SANS PROFESSION", "secretaire", "Secretaire", "SECRETAIRE", "secrétaire", "Secrétaire", "SECRETAIRE ADMINISTRATIVE", "Secrétaire administrative", "secretaire CNRS", "Secretaire Comptable", "SECRETAIRE COMPTABLE", "Secrétaire comptable", "Secrétaire de direction", "secrétaire générale d’organisation professionnelle", "SECRETAIRE JURIDIQUE", "SECRETAIRE MAIRIE", "Secrétaire médical aide opératoire", "SECRETAIRE MEDICALE", "Secrétaire médicale", "Secrétaire-comptable", "secrétaire, comptable", "Secrétaire/Comptable", "Serveuse", "SERVEUSE", "standardiste", "Standardiste sténo-dactylo", "TECHNICIEN ADMINISTRATIF", "technicienne", "TECHNICIENNE D’INTERVENTION SOCIALE ET FAMILLIALE", "Technicienne de laboratoire", "Technicienne de machine", "technicienne de maîtrise des eaux", "Technicienne france telecom", "TECHNICIENNE TERRITORIAL", "vendeuse", "Vendeuse", "VETERINAIRE", "virement de riz entre france et angleterre à la bourse", "Viticultrice".
fa_mere_qual
| Variable type |
integer |
| Number of missing obs. |
228 (50.22 %) |
| Number of unique values |
9 |
| Median |
8 |
| 1st and 3rd quartiles |
6; 9 |
| Min. and max. |
1; 9 |
fa_mere_statut
| Variable type |
integer |
| Number of missing obs. |
146 (32.16 %) |
| Number of unique values |
4 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 99 |
fa_pm_union_int
| Variable type |
integer |
| Number of missing obs. |
140 (30.84 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_pere_an_deces
| Variable type |
integer |
| Number of missing obs. |
411 (90.53 %) |
| Number of unique values |
22 |
| Median |
2007 |
| 1st and 3rd quartiles |
1997.5; 2014.5 |
| Min. and max. |
1984; 2018 |
fa_pere_an_nais
| Variable type |
integer |
| Number of missing obs. |
121 (26.65 %) |
| Number of unique values |
53 |
| Median |
1957 |
| 1st and 3rd quartiles |
1950; 1963 |
| Min. and max. |
1905; 1978 |
fa_pere_decede
| Variable type |
integer |
| Number of missing obs. |
113 (24.89 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_pere_entreprise
| Variable type |
integer |
| Number of missing obs. |
196 (43.17 %) |
| Number of unique values |
5 |
| Median |
5 |
| 1st and 3rd quartiles |
4; 5 |
| Min. and max. |
1; 5 |
fa_pere_etudes
| Variable type |
integer |
| Number of missing obs. |
121 (26.65 %) |
| Number of unique values |
12 |
| Median |
8 |
| 1st and 3rd quartiles |
4; 13 |
| Min. and max. |
1; 99 |
fa_pere_mois_deces
| Variable type |
integer |
| Number of missing obs. |
410 (90.31 %) |
| Number of unique values |
12 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 9.25 |
| Min. and max. |
1; 12 |
fa_pere_mois_nais
| Variable type |
integer |
| Number of missing obs. |
115 (25.33 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 10 |
| Min. and max. |
1; 12 |
fa_pere_prenom
| Variable type |
character |
| Number of missing obs. |
110 (24.23 %) |
| Number of unique values |
133 |
| Mode |
“JE” |
- Observed factor levels: "AB", "AD", "AH", "al", "aL", "Al", "AL", "an", "AN", "Ar", "AU", "AY", "be", "Be", "BE", "br", "BR", "BU", "ce", "ch", "Ch", "CH", "cl", "Cl", "CL", "da", "Da", "DA", "De", "DE", "di", "Di", "DI", "do", "Do", "DO", "ED", "em", "Em", "EM", "Er", "ER", "Eu", "Fa", "FA", "fc", "fe", "fr", "Fr", "FR", "ga", "GA", "ge", "Ge", "GE", "gi", "Gi", "GI", "gu", "Gu", "GU", "HA", "he", "He", "HE", "HU", "Ja", "JA", "Jc", "JC", "je", "Je", "JE", "JF", "Jm", "JM", "jo", "JO", "JP", "Ju", "KA", "la", "La", "LA", "LE", "Li", "LI", "lo", "lu", "Lu", "ma", "Ma", "MA", "mi", "Mi", "MI", "NI", "NO", "ol", "Ol", "OL", "pa", "Pa", "PA", "ph", "Ph", "PH", "pi", "Pi", "PI", "QU", "RA", "re", "Re", "RE", "Ro", "RO", "Sa", "SA", "SE", "st", "ST", "sy", "Sy", "SY", "th", "TH", "UR", "VI", "Ye", "yv", "Yv", "YV".
fa_pere_prof
| Variable type |
character |
| Number of missing obs. |
125 (27.53 %) |
| Number of unique values |
278 |
| Mode |
“Agriculteur” |
- Observed factor levels: "adjoint administration pénitentiaire", "Agent à la banque postale", "agent d’enquete service social", "Agent d’entretien", "Agent de maitrise", "AGENT DE MAITRISE", "agent de maîtrise", "Agent de métrologie", "Agent de recouvrement", "agent de securité", "agent de sécurité", "Agent dispatcheur EDF", "agent sncf", "Agent sncf", "Agent SNCF", "agent territorial", "agriculteur", "Agriculteur", "agriculteur exploitant", "ambulancier", "Ambulancier", "AMBULANCIER", "archiviste", "Artisan couvreur chauffagiste", "Artisan maçon", "Artisan staffeur ornemaniste", "Avocat", "AVS", "bagagiste", "biologiste", "Bonnetier", "boucher", "Boucher", "boulanger", "Boulanger", "boulanger patissier", "boulanger- patissier", "Brocanteurs", "Cadre", "Cadre conception prototype", "Cadre contrat informatique", "Cadre d’entreprise", "Cadre de la fonction publique", "CADRE DIRIGEANT", "cadre exploitant", "Cadre industrie beton, responsable de bureau d ’etude", "Cadre infirmier", "CADRE MEDICO SOCIAL", "CADRE RH CPAM RHONE", "Cadre salarié", "Cadre supérieur", "Cadre sur plateforme pétrolière", "cadre technique", "Cadre Telecom", "caméraman", "celier", "Charge d?affaire", "Chargé d’affaires", "charpentier de marine", "chaudronnier", "chauffeur", "Chauffeur de car", "chauffeur livreur et dératiseur", "CHAUFFEUR PL", "chauffeur pour les HUS de Strasbourg", "Chauffeur routier", "CHEF CUISINIER", "Chef d’atelier tourneur fraiseur", "chef d’entreprise", "Chef d’entreprise", "CHEF D’ENTREPRISE", "Chef d’équipe en centrale nucléaire", "Chef de brigade", "chef de chantier", "Chef de livraison", "CHEF DE SERVICE", "Chef de travaux DCN", "cheminot", "Chercheur", "chercheur en mathématiques", "CHIRURGIEN", "chirurgien dentiste", "Chirurgien dentiste", "chomeur", "clerc d’huissier de justice", "Co-Directeur d’une société de consulting informatique", "Commercant", "Commerçant", "commercial", "Commercial", "COMMERCIAL", "Comptable", "COMPTABLE", "CONDUCTEUR D ENGINS BTP", "Conducteur de bus", "Conducteur de bus et tramway", "Conducteur de train", "conducteur de travaux", "CONSEILLER DE VENTE", "Conseiller téléphonique à la poste", "contremaitre", "contrôleur de gestion", "Contrôleur de gestion", "CORDONNIER", "Courtier maritime", "COUVREUR", "Dépanneur tv", "dermatologue", "dessinateur industriel", "Dessinateur industriel", "Dessinateur projeteur", "Directeur", "DIRECTEUR CFA", "Directeur commercial", "Directeur d’agence de nettoyage en France", "directeur d’établissement", "Directeur d’exploitation", "Directeur de gestion", "Directeur de piscine", "Directeur de production", "DIRECTEUR DES ETUDES AVANCEES", "Directeur des systèmes d’information", "directeur générale ecole supérieure", "Directeur Recherche et développement", "Directeur technique", "Direction opération", "direction ramassage ordure", "Docker", "Docteur en PHARMACIE", "documentaliste", "DRH", "Econome", "Electricien", "ELECTRICIEN", "employé", "Employé à la nouvel republique", "employé administratif", "Employé aux espaces verts", "employé de banque", "Employé de banque", "employé en usine", "Employé municipal", "Employé PTT", "Employé SNCF", "enseignant", "Enseignant", "Enseignant référent", "entrepreneur en transports", "Etireur cuivre", "Evenementiel", "Fonctionnaire", "formateur", "gardien d’immeuble", "Gardien de HLM", "Gardien de la paix", "gendarme", "GENDARME", "géomètre", "Gérant d’entreprise", "gerant de societe", "GERANT ENTREPRISE", "Gerant societe", "GERANT SOCIETE DE LOCATION DE VOITURE", "gestionnaire de banque", "Gestionnaire locatif", "grutier", "imprimeur", "Inconnue", "infirmier", "INFIRMIER", "Informaticien", "Ingenieur", "INGENIEUR", "ingénieur", "Ingénieur", "Ingénieur Agronome", "INGENIEUR AUTOMOBILE", "Ingénieur cadre supérieur", "Ingénieur du son", "Ingénieur en chef en batiment", "Ingénieur en pétrochimie", "ingénieur fonction publique", "INGENIEUR HORTICOLE", "instituteur", "journaliste", "Journaliste", "kiné", "KINESITHERAPEUTE", "kinesithérapeute", "kinésithérapeute", "MACON", "maçon", "Maçon", "MACONNERIE", "Magasinier", "magasinier, aprovisionneur", "MANUTENTIONNAIRE", "mecanicien", "Mecanicien", "Medecin", "médecin généraliste", "médecin radiologue", "medecin urgentiste", "MENUISIER", "militaire", "Militaire", "MILITAIRE", "MILITAIRE A LA RETRAITE", "MINEUR", "Moniteur auto-école", "Monteur réseau télécom", "Musicien", "OFFICIER DE MARINE", "Officier de Police Judiciaire", "Opérateur de marché", "ouvrier", "Ouvrier", "OUVRIER", "ouvrier dans le bâtiment", "Ouvrier de métrologie", "ouvrier de scierie", "Ouvrier en métallurgie", "Ouvrier logistique", "ouvrier qualifié", "PATISSIER", "Paysagiste", "PAYSAGISTE", "Pdg", "PDG", "Peintre en bâtiment", "photographe", "Plannificateur", "plaquiste", "plombier", "Plombier", "Plombier chauffagiste", "PLOMPBIER", "Pompiers", "Porteur DNA", "producteur de myrtille", "Professeur", "PROFESSEUR", "REMORQUEUR VL", "RESPONSABLE COMMERCIAL", "responsable de production dans l’agencement de magasin", "Responsable du service gestion des stocks", "Responsable du service intérieur à l’ENSAPM", "responsable environnement sncf", "Responsable service construction", "Restaurateur", "ROUTIER", "Sapeur Pompier", "secrétaire général", "Sérigraphe", "serveur", "Serveur", "Superviseur de production", "Surveillant d’établissement pénitentiaire", "Technicien", "TECHNICIEN", "Technicien méthodes", "TECHNICIEN SAV CAMPING", "Technicien spécialisé", "TECHNICIEN SUPERIEUR", "Technicien supérieur", "technicien supérieur pour TDF", "Technico commercial", "tourneur", "Tourneur fraiseur", "Trader", "Travail dans une usine", "tuyauteur", "vendeur", "Vendeur", "Veterinaire", "vétérinaire", "Viticulteur", "ZINGUEUR".
fa_pere_qual
| Variable type |
integer |
| Number of missing obs. |
206 (45.37 %) |
| Number of unique values |
9 |
| Median |
6 |
| 1st and 3rd quartiles |
2; 7 |
| Min. and max. |
1; 9 |
fa_pere_statut
| Variable type |
integer |
| Number of missing obs. |
118 (25.99 %) |
| Number of unique values |
4 |
| Median |
3 |
| 1st and 3rd quartiles |
3; 3 |
| Min. and max. |
1; 99 |
fa_pm_union_int_muco
| Variable type |
integer |
| Number of missing obs. |
355 (78.19 %) |
| Number of unique values |
3 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 99 |
fa_rang_nais
| Variable type |
integer |
| Number of missing obs. |
179 (39.43 %) |
| Number of unique values |
8 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 9 |
fa_pm_union_an
| Variable type |
integer |
| Number of missing obs. |
184 (40.53 %) |
| Number of unique values |
56 |
| Median |
1982 |
| 1st and 3rd quartiles |
1975; 1989 |
| Min. and max. |
1928; 2006 |
fa_pm_union_mois
| Variable type |
integer |
| Number of missing obs. |
203 (44.71 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
fa_pm_union
| Variable type |
integer |
| Number of missing obs. |
114 (25.11 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 3 |
fa_couple_1an
| Variable type |
integer |
| Number of missing obs. |
107 (23.57 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0.5; 1 |
| Min. and max. |
0; 1 |
fa_enfants
| Variable type |
integer |
| Number of missing obs. |
121 (26.65 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_enf_adopte_nb
| Variable type |
integer |
| Number of missing obs. |
156 (34.36 %) |
| Number of unique values |
3 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 2 |
fa_enf_bio_nb
| Variable type |
integer |
| Number of missing obs. |
155 (34.14 %) |
| Number of unique values |
4 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 3 |
fa_fratrie_adopte_nb
| Variable type |
integer |
| Number of missing obs. |
148 (32.6 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_fratrie_bio_nb
| Variable type |
integer |
| Number of missing obs. |
133 (29.3 %) |
| Number of unique values |
9 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
0; 11 |
fa_fratrie_demi_nb
| Variable type |
integer |
| Number of missing obs. |
141 (31.06 %) |
| Number of unique values |
8 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 9 |
fa_matrimoniale
| Variable type |
integer |
| Number of missing obs. |
94 (20.7 %) |
| Number of unique values |
6 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 3 |
| Min. and max. |
1; 6 |
fa_type
- The variable only takes one (non-missing) value: "P". The variable contains 17.84 % missing observations.
id_fa_cat
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
3 |
| Mode |
“id_01_fa_04” |
- Observed factor levels: "fa_04", "id_01", "id_01_fa_04".
fa_fo_revenus_corr
| Variable type |
character |
| Number of missing obs. |
182 (40.09 %) |
| Number of unique values |
5 |
| Mode |
“C-[28.000 - <45.400]” |
- Observed factor levels: "A-[<7.200 - <17.170]", "B-[17.170 - <28.000]", "C-[28.000 - <45.400]", "D-[45.400 - >104.550]", "E-[NSPR - NSP]".
fa_cpl_nb
| Variable type |
integer |
| Number of missing obs. |
219 (48.24 %) |
| Number of unique values |
4 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 4 |
fa_couple_date_creation
| Variable type |
character |
| Number of missing obs. |
219 (48.24 %) |
| Number of unique values |
130 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-19", "2017-04-20", "2017-04-22", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-15", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-22", "2017-06-13", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-28", "2017-06-29", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-11", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-24", "2017-07-27", "2017-07-28", "2017-07-30", "2017-08-03", "2017-08-08", "2017-08-10", "2017-08-17", "2017-08-30", "2017-09-02", "2017-09-06", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-29", "2017-11-02", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-23", "2018-01-27", "2018-02-01", "2018-02-04", "2018-02-08", "2018-02-12", "2018-02-25", "2018-03-09", "2018-03-12", "2018-04-02", "2018-05-03", "2018-05-13", "2018-05-16", "2018-07-26", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-03", "2018-12-04", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-21", "2018-12-24", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-07", "2019-01-08", "2019-01-10", "2019-01-14", "2019-01-17", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-02-01", "2019-02-14", "2019-02-18".
fa_couple_type
- The variable only takes one (non-missing) value: "P". The variable contains 48.24 % missing observations.
fa_couple_cat
| Variable type |
character |
| Number of missing obs. |
222 (48.9 %) |
| Number of unique values |
2 |
| Mode |
“fa_04_couple_04” |
- Observed factor levels: "couple_04", "fa_04_couple_04".
fa_cpl01_conjt_union_an
| Variable type |
integer |
| Number of missing obs. |
222 (48.9 %) |
| Number of unique values |
35 |
| Median |
2007 |
| 1st and 3rd quartiles |
2002; 2011 |
| Min. and max. |
1972; 2017 |
fa_cpl02_conjt_union_an
| Variable type |
integer |
| Number of missing obs. |
390 (85.9 %) |
| Number of unique values |
17 |
| Median |
2012 |
| 1st and 3rd quartiles |
2009; 2014.25 |
| Min. and max. |
1997; 2018 |
fa_cpl03_conjt_union_an
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
7 |
| Median |
2015 |
| 1st and 3rd quartiles |
2013; 2016 |
| Min. and max. |
2010; 2017 |
fa_cpl04_conjt_union_an
- The variable only takes one (non-missing) value: "2017". The variable contains 99.78 % missing observations.
fa_cpl01_conjt_union_mois
| Variable type |
integer |
| Number of missing obs. |
230 (50.66 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
fa_cpl02_conjt_union_mois
| Variable type |
integer |
| Number of missing obs. |
391 (86.12 %) |
| Number of unique values |
10 |
| Median |
7 |
| 1st and 3rd quartiles |
5; 9 |
| Min. and max. |
1; 11 |
fa_cpl03_conjt_union_mois
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
11 |
| Median |
6 |
| 1st and 3rd quartiles |
3; 9 |
| Min. and max. |
1; 12 |
fa_cpl04_conjt_union_mois
- The variable only takes one (non-missing) value: "6". The variable contains 99.78 % missing observations.
fa_cpl01_int_an
| Variable type |
integer |
| Number of missing obs. |
364 (80.18 %) |
| Number of unique values |
19 |
| Median |
2011 |
| 1st and 3rd quartiles |
2008; 2014 |
| Min. and max. |
1991; 2018 |
fa_cpl02_int_an
| Variable type |
integer |
| Number of missing obs. |
427 (94.05 %) |
| Number of unique values |
12 |
| Median |
2015 |
| 1st and 3rd quartiles |
2011.5; 2015 |
| Min. and max. |
2003; 2018 |
fa_cpl03_int_an
| Variable type |
integer |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
5 |
| Median |
2016 |
| 1st and 3rd quartiles |
2015; 2017 |
| Min. and max. |
2013; 2018 |
fa_cpl04_int_an
- The variable only takes one value: "NA".
fa_cpl01_conjt_nais_an
| Variable type |
integer |
| Number of missing obs. |
221 (48.68 %) |
| Number of unique values |
41 |
| Median |
1984 |
| 1st and 3rd quartiles |
1978; 1989 |
| Min. and max. |
1944; 1999 |
fa_cpl02_conjt_nais_an
| Variable type |
integer |
| Number of missing obs. |
389 (85.68 %) |
| Number of unique values |
26 |
| Median |
1985 |
| 1st and 3rd quartiles |
1982; 1989 |
| Min. and max. |
1963; 1997 |
fa_cpl03_conjt_nais_an
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
14 |
| Median |
1986 |
| 1st and 3rd quartiles |
1983; 1990 |
| Min. and max. |
1975; 1992 |
fa_cpl04_conjt_nais_an
- The variable only takes one (non-missing) value: "1981". The variable contains 99.78 % missing observations.
fa_cpl01_conjt_enf
| Variable type |
integer |
| Number of missing obs. |
220 (48.46 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_cpl02_conjt_enf
| Variable type |
integer |
| Number of missing obs. |
389 (85.68 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl03_conjt_enf
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl04_conjt_enf
- The variable only takes one (non-missing) value: "0". The variable contains 99.78 % missing observations.
fa_cpl01_conjt_nais_mois
| Variable type |
integer |
| Number of missing obs. |
221 (48.68 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
fa_cpl02_conjt_nais_mois
| Variable type |
integer |
| Number of missing obs. |
389 (85.68 %) |
| Number of unique values |
12 |
| Median |
6 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
fa_cpl03_conjt_nais_mois
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
12 |
| Median |
5 |
| 1st and 3rd quartiles |
2; 8 |
| Min. and max. |
1; 12 |
fa_cpl04_conjt_nais_mois
- The variable only takes one (non-missing) value: "3". The variable contains 99.78 % missing observations.
fa_cpl01_conjt_muco
| Variable type |
integer |
| Number of missing obs. |
221 (48.68 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl02_conjt_muco
| Variable type |
integer |
| Number of missing obs. |
390 (85.9 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl03_conjt_muco
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl04_conjt_muco
- The variable only takes one (non-missing) value: "0". The variable contains 99.78 % missing observations.
fa_cpl01_conjt_prenom
| Variable type |
character |
| Number of missing obs. |
227 (50 %) |
| Number of unique values |
124 |
| Mode |
“MA” |
- Observed factor levels: "Ad", "AD", "Ai", "Al", "AL", "am", "AM", "An", "AN", "Ar", "AR", "au", "Au", "AU", "ba", "be", "BE", "br", "Br", "BR", "Ca", "CA", "ce", "Ce", "CE", "ch", "Ch", "CH", "cl", "Cl", "da", "Da", "DA", "de", "DE", "DO", "El", "EL", "EM", "er", "ER", "es", "ES", "FA", "fl", "FL", "fr", "Fr", "FR", "GA", "GE", "Gu", "GU", "GW", "He", "HE", "Hu", "ja", "je", "JE", "jo", "JO", "ju", "Ju", "JU", "KE", "La", "LA", "li", "LI", "Lo", "LO", "lu", "LU", "ma", "Ma", "MA", "Me", "ME", "Mi", "MI", "mu", "Mu", "Ni", "NI", "no", "Ol", "Op", "pa", "PA", "pe", "ph", "PH", "pi", "Pi", "PO", "RA", "RE", "ru", "sa", "SA", "se", "Se", "SE", "SO", "st", "ST", "Th", "TH", "ti", "Ti", "to", "tr", "TR", "va", "VA", "vi", "VI", "wi", "ws", "XA", "Ya", "YA", "YO".
fa_cpl02_conjt_prenom
| Variable type |
character |
| Number of missing obs. |
390 (85.9 %) |
| Number of unique values |
52 |
| Mode |
“MA” |
- Observed factor levels: "al", "AL", "AN", "AR", "AT", "au", "Au", "be", "BE", "BI", "ca", "CA", "CE", "ch", "Ch", "CL", "cy", "CY", "DA", "di", "DO", "EM", "ES", "FA", "FR", "Ge", "HU", "In", "je", "Je", "JE", "ju", "Ju", "la", "LI", "LO", "LU", "MA", "ME", "PA", "PI", "RA", "ro", "Ro", "SE", "SO", "st", "ST", "TH", "va", "Va", "XA".
fa_cpl03_conjt_prenom
| Variable type |
character |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
21 |
| Mode |
“Al” |
- Observed factor levels: "Al", "Am", "AM", "BE", "CA", "CE", "ch", "CL", "EL", "GU", "Ju", "JU", "LA", "LO", "MA", "NI", "TE", "Th", "TO", "tr", "vi".
fa_cpl04_conjt_prenom
- The variable only takes one (non-missing) value: "EM". The variable contains 99.78 % missing observations.
fa_cpl01_conjt_sexe
| Variable type |
integer |
| Number of missing obs. |
223 (49.12 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
fa_cpl02_conjt_sexe
| Variable type |
integer |
| Number of missing obs. |
390 (85.9 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
fa_cpl03_conjt_sexe
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
fa_cpl04_conjt_sexe
- The variable only takes one (non-missing) value: "2". The variable contains 99.78 % missing observations.
fa_cpl01_int
| Variable type |
integer |
| Number of missing obs. |
222 (48.9 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_cpl02_int
| Variable type |
integer |
| Number of missing obs. |
389 (85.68 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_cpl03_int
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_cpl04_int
- The variable only takes one (non-missing) value: "0". The variable contains 99.78 % missing observations.
fa_cpl01_int_par
| Variable type |
integer |
| Number of missing obs. |
363 (79.96 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
2; 2 |
| Min. and max. |
1; 3 |
fa_cpl02_int_par
| Variable type |
integer |
| Number of missing obs. |
427 (94.05 %) |
| Number of unique values |
2 |
| Median |
2 |
| 1st and 3rd quartiles |
2; 2 |
| Min. and max. |
1; 2 |
fa_cpl03_int_par
- The variable only takes one (non-missing) value: "2". The variable contains 98.02 % missing observations.
fa_cpl04_int_par
- The variable only takes one value: "NA".
fa_cpl01_int_mois
| Variable type |
integer |
| Number of missing obs. |
367 (80.84 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 9 |
| Min. and max. |
1; 12 |
fa_cpl02_int_mois
| Variable type |
integer |
| Number of missing obs. |
427 (94.05 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
3.5; 9.5 |
| Min. and max. |
1; 12 |
fa_cpl03_int_mois
| Variable type |
integer |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
6 |
| Median |
9 |
| 1st and 3rd quartiles |
7; 10 |
| Min. and max. |
3; 12 |
fa_cpl04_int_mois
- The variable only takes one value: "NA".
fa_cpl01_union_nature
| Variable type |
integer |
| Number of missing obs. |
220 (48.46 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 3 |
fa_cpl02_union_nature
| Variable type |
integer |
| Number of missing obs. |
389 (85.68 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
2; 2 |
| Min. and max. |
1; 3 |
fa_cpl03_union_nature
| Variable type |
integer |
| Number of missing obs. |
433 (95.37 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
2; 2 |
| Min. and max. |
1; 3 |
fa_cpl04_union_nature
- The variable only takes one (non-missing) value: "2". The variable contains 99.78 % missing observations.
fa_cpl01_int_muco
| Variable type |
integer |
| Number of missing obs. |
363 (79.96 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl02_int_muco
| Variable type |
integer |
| Number of missing obs. |
427 (94.05 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_cpl03_int_muco
| Variable type |
integer |
| Number of missing obs. |
445 (98.02 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_cpl04_int_muco
- The variable only takes one value: "NA".
fa_enf_nb
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 3 |
fa_enfants_date_creation
| Variable type |
character |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
63 |
| Mode |
“2018-12-11” |
- Observed factor levels: "2017-04-10", "2017-04-11", "2017-05-04", "2017-05-07", "2017-05-09", "2017-05-22", "2017-05-24", "2017-06-02", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-28", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-18", "2017-07-22", "2017-07-24", "2017-08-08", "2017-09-02", "2017-09-25", "2017-10-29", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-30", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-19", "2017-12-28", "2018-01-02", "2018-01-29", "2018-02-01", "2018-02-25", "2018-03-09", "2018-05-16", "2018-08-09", "2018-09-11", "2018-11-22", "2018-11-27", "2018-11-28", "2018-12-03", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-17", "2018-12-21", "2018-12-27", "2018-12-30", "2018-12-31", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-07", "2019-01-17", "2019-01-27".
fa_enfants_type
- The variable only takes one (non-missing) value: "P". The variable contains 82.82 % missing observations.
fa_enfants_cat
- The variable only takes one (non-missing) value: "fa_04_enfants_04". The variable contains 83.04 % missing observations.
fa_enf_nb_nat
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
4 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 3 |
fa_enf_nb_med
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
4 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 3 |
fa_enf_nb_cjt
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
3 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 2 |
fa_enf_nb_adp
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_enf01_nb_par_nais
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
fa_enf02_nb_par_nais
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
fa_enf03_nb_par_nais
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
fa_enf01_nais_an
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
25 |
| Median |
2009 |
| 1st and 3rd quartiles |
2003; 2014 |
| Min. and max. |
1964; 2018 |
fa_enf02_nais_an
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
20 |
| Median |
2008 |
| 1st and 3rd quartiles |
2005; 2014 |
| Min. and max. |
1966; 2017 |
fa_enf03_nais_an
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Median |
2011.5 |
| 1st and 3rd quartiles |
2008.75; 2012.75 |
| Min. and max. |
1978; 2017 |
fa_enf01_nais_mois
| Variable type |
integer |
| Number of missing obs. |
377 (83.04 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
4; 10 |
| Min. and max. |
1; 12 |
fa_enf02_nais_mois
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
11 |
| Median |
6 |
| 1st and 3rd quartiles |
5; 10 |
| Min. and max. |
1; 12 |
fa_enf03_nais_mois
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
4 |
| Median |
2.5 |
| 1st and 3rd quartiles |
2; 4.5 |
| Min. and max. |
2; 10 |
fa_enf01_prenom
| Variable type |
character |
| Number of missing obs. |
377 (83.04 %) |
| Number of unique values |
52 |
| Mode |
“MA” |
- Observed factor levels: "Ad", "AD", "AN", "BA", "BE", "Ca", "Ce", "CE", "Cé", "Cl", "CL", "CO", "do", "Eb", "el", "El", "EL", "Em", "En", "ib", "In", "JE", "Jo", "JU", "Ka", "LA", "Le", "LE", "Li", "Lo", "LO", "MA", "me", "ME", "ni", "no", "NO", "NY", "RA", "Ro", "RO", "sa", "Sa", "th", "TH", "Ti", "TI", "TO", "VA", "Wa", "Ya", "yl".
fa_enf02_prenom
| Variable type |
character |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
28 |
| Mode |
“Ma” |
- Observed factor levels: "al", "AM", "Au", "BA", "BE", "ca", "CA", "Ch", "CH", "Em", "Fi", "FL", "Gu", "JE", "Lé", "li", "lo", "LO", "LU", "ma", "Ma", "MA", "ro", "Th", "TH", "TO", "ve", "ZO".
fa_enf03_prenom
| Variable type |
character |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Mode |
“BL” |
- Observed factor levels: "BL", "LE", "lo", "lu", "ro", "Ta".
fa_enf01_adopte_an
| Variable type |
integer |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Median |
2005 |
| 1st and 3rd quartiles |
1999.75; 2009.5 |
| Min. and max. |
1990; 2017 |
fa_enf02_adopte_an
- The variable only takes one value: "NA".
fa_enf03_adopte_an
- The variable only takes one value: "NA".
fa_enf01_adopte_mois
| Variable type |
integer |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
3 |
| Median |
5.5 |
| 1st and 3rd quartiles |
1; 10.5 |
| Min. and max. |
1; 12 |
fa_enf02_adopte_mois
- The variable only takes one value: "NA".
fa_enf03_adopte_mois
- The variable only takes one value: "NA".
fa_enf01_deces_an
| Variable type |
integer |
| Number of missing obs. |
451 (99.34 %) |
| Number of unique values |
3 |
| Median |
2004 |
| 1st and 3rd quartiles |
1995.5; 2010.5 |
| Min. and max. |
1987; 2017 |
fa_enf02_deces_an
- The variable only takes one value: "NA".
fa_enf03_deces_an
- The variable only takes one value: "NA".
fa_enf01_conjt_prenom
| Variable type |
character |
| Number of missing obs. |
383 (84.36 %) |
| Number of unique values |
55 |
| Mode |
“CH” |
- Observed factor levels: "AD", "An", "au", "BE", "BR", "CA", "Ce", "CE", "ch", "Ch", "CH", "cl", "DE", "DO", "El", "EL", "ES", "fl", "FL", "Fr", "FR", "ge", "GE", "Gu", "HE", "HU", "is", "JE", "JM", "JO", "Ju", "La", "LO", "LU", "ma", "Ma", "MA", "Me", "Mi", "Na", "Ni", "NI", "Pa", "ph", "Pi", "Ro", "SA", "Se", "SO", "St", "TH", "to", "VA", "vi", "wi".
fa_enf02_conjt_prenom
| Variable type |
character |
| Number of missing obs. |
427 (94.05 %) |
| Number of unique values |
25 |
| Mode |
“La” |
- Observed factor levels: "AD", "au", "BR", "Ce", "Ch", "CH", "DE", "FL", "fr", "FR", "HE", "HU", "is", "JM", "Ju", "la", "La", "LI", "ma", "Ma", "Pi", "Se", "SO", "TH", "vi".
fa_enf03_conjt_prenom
| Variable type |
character |
| Number of missing obs. |
449 (98.9 %) |
| Number of unique values |
5 |
| Mode |
“Ce” |
- Observed factor levels: "Ce", "FL", "FR", "is", "ma".
fa_enf01_decede
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_enf02_decede
- The variable only takes one (non-missing) value: "0". The variable contains 93.61 % missing observations.
fa_enf03_decede
- The variable only takes one (non-missing) value: "0". The variable contains 98.68 % missing observations.
fa_enf01_diag_avt
| Variable type |
integer |
| Number of missing obs. |
377 (83.04 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_enf02_diag_avt
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
fa_enf03_diag_avt
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0.25; 1 |
| Min. and max. |
0; 1 |
fa_enf01_issu
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
4 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 4 |
fa_enf02_issu
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 3 |
fa_enf03_issu
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
fa_enf01_deces_mois
| Variable type |
integer |
| Number of missing obs. |
452 (99.56 %) |
| Number of unique values |
2 |
| Median |
10 |
| 1st and 3rd quartiles |
9; 11 |
| Min. and max. |
8; 12 |
fa_enf02_deces_mois
- The variable only takes one value: "NA".
fa_enf03_deces_mois
- The variable only takes one value: "NA".
fa_enf01_muco
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_enf02_muco
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
fa_enf03_muco
- The variable only takes one (non-missing) value: "0". The variable contains 98.68 % missing observations.
fa_enf01_sexe
| Variable type |
integer |
| Number of missing obs. |
378 (83.26 %) |
| Number of unique values |
2 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
fa_enf02_sexe
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
2 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 2 |
fa_enf03_sexe
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1.75 |
| Min. and max. |
1; 2 |
fa_enf01_etat_mat
- The variable only takes one (non-missing) value: "Union". The variable contains 90.53 % missing observations.
fa_enf02_etat_mat
- The variable only takes one (non-missing) value: "Union". The variable contains 97.14 % missing observations.
fa_enf03_etat_mat
- The variable only takes one (non-missing) value: "Union". The variable contains 99.34 % missing observations.
fa_enf01_arrivee_an
| Variable type |
integer |
| Number of missing obs. |
376 (82.82 %) |
| Number of unique values |
26 |
| Median |
2009 |
| 1st and 3rd quartiles |
2003; 2014 |
| Min. and max. |
1964; 2018 |
fa_enf02_arrivee_an
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
20 |
| Median |
2008 |
| 1st and 3rd quartiles |
2005; 2014 |
| Min. and max. |
1966; 2017 |
fa_enf03_arrivee_an
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
6 |
| Median |
2011.5 |
| 1st and 3rd quartiles |
2008.75; 2012.75 |
| Min. and max. |
1978; 2017 |
fa_enf01_arrivee_mois
| Variable type |
integer |
| Number of missing obs. |
377 (83.04 %) |
| Number of unique values |
12 |
| Median |
7 |
| 1st and 3rd quartiles |
3; 10 |
| Min. and max. |
1; 12 |
fa_enf02_arrivee_mois
| Variable type |
integer |
| Number of missing obs. |
425 (93.61 %) |
| Number of unique values |
11 |
| Median |
6 |
| 1st and 3rd quartiles |
5; 10 |
| Min. and max. |
1; 12 |
fa_enf03_arrivee_mois
| Variable type |
integer |
| Number of missing obs. |
448 (98.68 %) |
| Number of unique values |
4 |
| Median |
2.5 |
| 1st and 3rd quartiles |
2; 4.5 |
| Min. and max. |
2; 10 |
lo_date_creation
| Variable type |
character |
| Number of missing obs. |
90 (19.82 %) |
| Number of unique values |
185 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-07", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-13", "2017-04-19", "2017-04-20", "2017-04-22", "2017-04-25", "2017-04-26", "2017-04-27", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-16", "2017-05-17", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-22", "2017-05-24", "2017-06-01", "2017-06-02", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-22", "2017-06-23", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-24", "2017-07-25", "2017-07-27", "2017-08-01", "2017-08-03", "2017-08-08", "2017-08-17", "2017-08-20", "2017-08-30", "2017-09-06", "2017-09-07", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-05", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-28", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-21", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-23", "2018-01-27", "2018-01-30", "2018-01-31", "2018-02-01", "2018-02-04", "2018-02-08", "2018-02-11", "2018-02-12", "2018-02-14", "2018-02-16", "2018-02-20", "2018-02-25", "2018-02-26", "2018-03-09", "2018-03-11", "2018-03-12", "2018-03-28", "2018-04-16", "2018-04-29", "2018-05-03", "2018-05-04", "2018-05-13", "2018-05-16", "2018-05-31", "2018-06-26", "2018-07-26", "2018-07-30", "2018-08-09", "2018-09-04", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-26", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-01", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-21", "2018-12-24", "2018-12-25", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-15", "2019-01-16", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-21", "2019-01-22", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-14", "2019-02-17", "2019-02-18", "2019-02-26", "2019-02-27", "2019-03-11", "2019-03-30".
lo_amenag_logement
| Variable type |
integer |
| Number of missing obs. |
92 (20.26 %) |
| Number of unique values |
3 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 2 |
lo_amenag_logement_precis
| Variable type |
character |
| Number of missing obs. |
407 (89.65 %) |
| Number of unique values |
47 |
| Mode |
“- Déménagement de ma chambre : passage du 1er étage au rez de chaussé, afin qu’elle soit de plein pied. - Trous dans le mur pour faire passer les tuyaux d’oxygène.” |
- Observed factor levels: "- Déménagement de ma chambre : passage du 1er étage au rez de chaussé, afin qu’elle soit de plein pied. - Trous dans le mur pour faire passer les tuyaux d’oxygène.", "Aménagement de la chambre, lit adapté", "AMENAGEMENTS PRATIQUES", "Avoir une salle de sport", "baignoire dans la salle de bain et aménagement d’un bureau avec tous les médicaments", "Chambre de pleins pieds", "CHAMBRE PARENTALE AU REZ DE CHAUSSEE", "Changer les moquette pour du parquet a cause de la poussière", "demenager, pour me rapprocher de l hopital et centre de greffe car je suis hospi a paris", "Douche au lieu de baignoire", "Enlèvent moisissures", "Faciliter au maximum les accès", "Faire plus de ménage pour éviter poussière et ce genre de chose", "installation d’une chambre, salle de bain au rez-de-chaussée, avec norme Handicap", "Installation d’une VMC", "isolation rénovation cuisine rénovation sanitaires", "Isolation. Enlever toutes les moquettes (murs et sols) pour les remplacer par du linoleum et de l’intissé à peindre.", "Je sens parfois des odeurs de cigarettes, le logement n’étant pas isolé de façon idéal . J’envisage donc de faire/refaire certains joints pour éviter que la fumée de cigarette ne m’atteigne (ne sachant pas s’il ne s’agit que d’odeur ou si les composants toxiques passent également).", "la mise en place de Climatisation, je souhaite avoir une piece de soin,", "lors de perfusion une piece est requisitionné pour les soins uniquement", "Maison avec peu d’escalier, création d’une salle de bain et d’une chambre en rez de chaussée", "maison neuve (humidité dans l’ancienne maison). ouverture plus grande adaptées aux handicapées.", "Meubles pour les traitements pour pouvoir les protéger de la poussière.", "Modification du système de chauffage (actuellement grilles pains pas bien bon côté respi). Installation clim.", "Nettoyage de l’aeration, changement des chauffages.", "Nous avons faire construire une entrée de pleins pieds pour les cas d?encombrement et de grosse fatigue.", "Nous avons tout aménagé au rez de chaussée pour que j’évite de monter les escaliers à l’époque où j’étais sous oxygène et VNI. L’étage nous sert de rangement mais je n’ai pas besoin d’y monter fréquemment.", "PEINTURE FONGICIDE", "Plain pied Chambre en plus à disposition pour les soins et oxygène", "Pose d’une VMC Remplacement du chauffage (poêle à fuel) Mise en place de climatisation Changement des fenêtres", "Prévoir de la place pour les cures d’antibiotiques à domicile (nombreux cartons, étagère,table) Prévoir de la place pour un vélo d’appartement, un gros ballon de rééducation Prévoir de la place pour nettoyer et faire sécher les aérosols et autres dispositifs (PEP? flutter, rhinohorn, rhinolaveur,…)", "PURIFICATEUR D’AIR CLIMATISEUR", "RDC", "Refaire la salle de bain", "remplacer les vieux volets par des volets roulants électriques.", "Retrait des tapisseries sur le mur et de la moquette", "salle de bain insalubre", "SALLE DE SOINS CABINET DE TOILETTE ADAPTE ACCESSIBILITE ETAGE", "Salle de sport et de soin", "Suppression de toutes les moquettes. Pose d’aérateur sur les fenêtrs", "Un espace réel dédié aux médicaments, aérosols et toute la place que cela prend", "Une chambre au rez de chaussé pour la bombonne d’oxygène.", "une chambre en bas", "Une pièce consacrée aux soins Une baignoire", "une place dans le salon pour effectuer mes cures ATB et un espace de stockage/pharmacie assez conséquente", "Une salle de soins avec table d?examens", "VMC".
lo_an_logement
| Variable type |
integer |
| Number of missing obs. |
126 (27.75 %) |
| Number of unique values |
34 |
| Median |
2014 |
| 1st and 3rd quartiles |
2009; 2017 |
| Min. and max. |
1981; 2019 |
lo_demenag
| Variable type |
integer |
| Number of missing obs. |
94 (20.7 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
lo_demenag_cause_1
- The variable only takes one (non-missing) value: "1". The variable contains 97.14 % missing observations.
lo_demenag_cause_2
- The variable only takes one (non-missing) value: "1". The variable contains 97.58 % missing observations.
lo_demenag_cause_999
- The variable only takes one (non-missing) value: "1". The variable contains 74.45 % missing observations.
lo_demenag_cause_autre
| Variable type |
character |
| Number of missing obs. |
343 (75.55 %) |
| Number of unique values |
109 |
| Mode |
“pour acheter” |
- Observed factor levels: "A long terme, pour me rapprocher du travail et éventuellement m’installer en couple.", "achat", "acheter une maison dans quelques années", "Afin d’avoir une pièce supplémentaire pour le stockage du matériel de soin", "Appartement plus grand quand je travaillerai", "Avoir mon chez moi dans le futur", "Avoir mon indépendance", "avoir mon propre logement", "besoin d’une chambre supplémentaire", "Car je vais partir étudier", "Changer de ville", "Cherche logement en autonomie (sans les parents)", "devenir propriétaire", "devenir propriétaire, problèmes avec le bailleur", "Emménagement à l’issue des travaux de rénovation. Aucun rapport avec la mucoviscidose.", "Enfants", "Être indépendant des parents et pouvoir inviter des amis librement, proche d’un futur employeur quand la santé le permettra (pour le moment, je privilégie la santé car celle-ci s’était dégradée lorsque je travaillais) et des commodités (sports, loisirs, soins…..). Difficulté d’acquisition d’un bien en location ou à l’achat à cause des contraintes liées à la fébrilité des intervenants immobiliers et à la fragilité des problèmes de santé qui ne permet pas d’avoir les garanties nécessaires pour être éligible et aux nombreuses barrières", "etre independent", "Être proche de mon travail.. Changement d’air", "greffée je peux et souhaite être plus indépendante", "habitat partagé à la campagne", "habitation loin des villes", "IL FAUDRA QUITTER LE LOGEMENT DE FONCTION UN JOUR", "indépendance", "logement + grand en vue de projet familial", "LOGEMENT HLM", "Loyer moins cher", "Loyer trop chers . Avoir mieux moins chers", "ma tante récupère son appartement je me mets en colloc avec des amis", "Maison indépendante", "maison plus grande", "Mobilité de travail dans le doubs", "mutation de mon pére a toulon", "Mutation Professionnelle à venir", "Par envie", "partir à l’étranger", "pas pour des raisons médicales", "plus d’espace, s’éloigner de la pollution de Paris", "plus grande maison", "Plus près de la campagne", "Pour acheté une maison plus grande.", "pour acheter", "Pour acheter", "pour acheter plus grand Je souhaiterais être aidée pour des informations sur les assurances des prêts", "Pour acheter pour avoir plus grand", "pour acheter un logement plus grand", "pour acheter un maison. J’emprunte sur 22 ans et je fais de fausses déclarations aux banques.", "pour aller dans la maison dont je suis propriétaire", "Pour aller vivre à la campagne", "pour avoir mon chez moi", "Pour avoir mon indépendance.", "Pour avoir mon propre appartement", "Pour avoir plus de place pour adapter mes soins, avoir plus d’espace pour ranger les appareils et médicaments. Actuellement tout est stocké dans la future chambre d’un bébé souhaité Et nous voudrions avoir une maison, a la campagne…mais le prêt serait plus important, donc j’ai peur de galérer à nouveau pour l’assurance de prêt qui a été 1 parcours laborieux la 1ere fois.", "Pour avoir plus de tranquillité et me rapprocher de ma famille", "Pour avoir un logement au RDC car là je suis au 1ier étage et c’est difficile par moment. Trop de bruit par rapport à mes voisins du coup je dors mal, et le sommeil est important !", "pour avoir un logement sur un seul niveau", "Pour avoir un loyer moins élevés.", "pour avoir une maison", "Pour changer de cadre de vie,se rapprocher de la campagne", "Pour construire notre maison dans plusieurs années.", "Pour continuer mes études dans une autre ville, où se trouve le diplôme d?État que j’envisage.", "Pour des raisons de confort personnel", "pour des raisons pécuniaires , trop de charges dû au mensualité du prêt et de l’assurance du prêt", "pour devenir propriétaire", "Pour du changement ou pour mes etudes", "Pour emménager après mon mariage en septembre avec mon fiancé", "pour emménager avec mon ami actuel", "Pour envisager un bébé d?ici quelques années", "Pour être indépendante", "Pour être près de l’université", "Pour me rapprocher de la mer, dans une région un peu moins polluée (Bretagne) qui sera un bénéfice pour ma greffe à venir. (Meilleur cadre de vie également)", "pour me rapprocher du CRCM et de l’ecole", "pour mes études", "Pour mes études", "POUR PLUS DE CONFORT", "pour plus grand", "Pour plus grand", "pour pouvoir avoir mon chez moi et ne plus vivre chez mes parents .", "Pour prendre mon indépendance", "Pour quelque chose de plus grand", "Pour réduire mon temps de trajet domicile-travail. Je passe un temps incroyable dans les bouchons matin et soir à cause de l’éloignement du centre ville. Ca me rajoute beaucoup de fatigue et de stress. J’ai donc le choix de changer de travail ou d’attendre de pouvoir changer de logement (d’ici 4 ans environ).", "pour une meilleure qualité de vie (logement plus grand / avec plus de pièces, dans un environnement qui nous plaît davantage et éventuellement un peu plus éloigné de la pollution urbaine).", "Pour vivre avec mon copain", "Pour vivre en campagne", "Pour vivre en couple", "Pour vivre en ville", "Pour vivre et avoir un logement avec mon concubin", "Prendre mon indépendance.", "Prendre plus grand pour avoir une chambre supplémentaire.", "Quand j’aurai un travail possibilité d’emménager avec mon cheri", "Quartier moins bruyant et moins pollué", "Quitter domicile familial", "raison economique pour une region moins chére", "raison financière", "Raison indépendante de la maladie", "Raisons personnelles", "Rapprochement familiale", "RAPPROCHEMENT FAMILLE ET CAMPAGNE", "Rejoindre mon Mari en logement de Gendarmerie dans le 89", "separation", "Souhait d’une maison avec cour et sans humidité", "souhaite vivre à la campagne", "Surface plus importante", "travail", "Trop petit.", "Un appartement au rdc où 1er étage maximum", "Vie privée", "Vivre indépendamment", "Vivre seuls".
lo_emprunt_1
- The variable only takes one (non-missing) value: "1". The variable contains 91.41 % missing observations.
lo_emprunt_2
- The variable only takes one (non-missing) value: "1". The variable contains 79.96 % missing observations.
lo_emprunt_3
- The variable only takes one (non-missing) value: "1". The variable contains 89.65 % missing observations.
lo_emprunt_4
- The variable only takes one (non-missing) value: "1". The variable contains 95.59 % missing observations.
lo_emprunt_5
- The variable only takes one (non-missing) value: "1". The variable contains 93.39 % missing observations.
lo_emprunt_6
- The variable only takes one (non-missing) value: "1". The variable contains 55.51 % missing observations.
lo_emprunt_precis
| Variable type |
character |
| Number of missing obs. |
435 (95.81 %) |
| Number of unique values |
19 |
| Mode |
“assurance proposée à prix très élevé et couvrant seulement le risque accident” |
- Observed factor levels: "assurance proposée à prix très élevé et couvrant seulement le risque accident", "Assurance. En 2007 la convention AERAS n’était pas votée. Que nous avons trouvé en nous rapprochant de l’association ACARAT.fr et qui nous a mis en relation avec un courtier d’assurance qui nous a permis d’assurer le capital emprunté. Depuis nous avons revendu ce bien.", "Cela a été très très difficile, par contre pour travailler à plein temps et payer mes impôts pleinement cela ne pose aucun problème.", "des ouvertures d’assurances vies et de comptes épargne en échange de l’acceptation du prêt", "en fait, je suis découragée par les assurances, risques et surprimes, j’ai abandonné. Mon compagnon étant propriétaire, pour le moment, nous ne faisons aucune démarche pour acheter quelque chose ensemble", "Financière", "FINANCIERE DU FAIT D’UN REVENU AMOINDRI PAR LE TRAVAIL A TEMPS PARTIEL DANS UNE REGION OU L’ACCESSION A LA PROPRIETE EST CHERE", "Financières", "Il manque : Non car vous n’avez pas dit à la banque que vous aviez la muco", "Interdit de faire une assurance décès", "Non assurée, crédit sur mon ami.", "notre âge, à moi et mon mari !", "pas d’assurance du tout pour moi refusé a tout les niveaux d’AERAS", "Prêt achat appartement accordé uniquement car ma mère c’est portée caution. Refus d’assurance pour raison de santé un greffé en 2009 est mort en 2011 m’a expliqué oralement ma banquière en 2012 (environ). Passer par la Convention AERAS revenait beaucoup trop cher, le coût du prêt était d’environ 10 à 15 %, contre 3,5% pour un bien-portant. Pas de prêt supérieur à 3000,00?. Pas de prêt accordé si questionnaire médical. Et on me dit qu il ne faut pas discriminer les personnes handicapées ???", "Probleme d assurance et de caution", "PROBLEME DE TAUX TROP ELEVES", "Refus de prêt car pas d’assurance", "Seuls les revenus de mon mari ont été comptés et l’assurance décès ne concerne que lui (pas moi)", "solvabilité".
lo_occup_cause_1
- The variable only takes one (non-missing) value: "1". The variable contains 91.19 % missing observations.
lo_occup_cause_2
- The variable only takes one (non-missing) value: "1". The variable contains 93.61 % missing observations.
lo_occup_cause_3
- The variable only takes one (non-missing) value: "1". The variable contains 74.45 % missing observations.
lo_occup_cause_999
- The variable only takes one (non-missing) value: "1". The variable contains 57.27 % missing observations.
lo_occup_cause_autre
| Variable type |
character |
| Number of missing obs. |
265 (58.37 %) |
| Number of unique values |
184 |
| Mode |
“parce qu’il nous plaisait” |
- Observed factor levels: "-", "à l époque je ne savais pas que j ’étais malade", "achat avant naissance", "adéquation du bien avec nos aspiratons", "ancien logement humide", "appartement de ma tante facile et économique", "appartement de mes parents qui n’était plus en location", "Appartement de mon concubin", "assez de pièces pour avoir bureau pour télétravail", "Aucun lien entre choix du logement et mucoviscidose", "Aucune raison précise", "Avoir mon indépendance", "Avoir un petit terrain", "Bien placé niveau travail et hôpital", "Bonne situation géographique", "C’est le choix des mes parents", "c’était celui de mon conjoint propriétaire", "Cadre de vie, campagne, proche du lieu de travail", "Campagne", "CAMPAGNE CALME", "car il me plaisait.", "Car j?étais Hébergé", "Ce sont mes parents qui ont choisi ce logement avec comme critères : proximité du travail de mon père, des lycées-collège, des transports et de la ville… L’environnement a compté également (verdure, petite ville bien fréquentée)", "centre ville proximité commerce et écoles", "Cest la maison de mes parents", "Changement de travail dans une nouvelle région", "chez mon frere", "Choix de vie à la campagne", "CHOIX PERSONNEL SANS RAPPORT AVEC LA PATHOLOGIE", "choix stratégique par rapport au lieu de travail", "confort", "confort de vie (campagne)", "Confort familial", "CONSTRUCTION DE LA MAISON", "Coup de c?ur", "coup de c?ur, environnement, proximité des services…", "coup de coeur", "critères persos", "Dans une ville et un quartier qui me plaisait, pour ne pas avoir à faire d’allers-retours importants (si j’avais vécu en dehors de la ville), mais aussi au 1er étage pour éviter d’être essouflé en rentrant !", "distance avec mon lieu d’emploi", "du boulot", "En attendant de trouver mon premier emploi", "en fonction des lieux de travail", "Envie d’une Maison", "Etudes", "Financièrement en attendant de trouver du travail", "FINANCIEREMENT ET ETRE AU CALME", "finir mes études tranquillement", "Gratuité, travaux en 2009 pour aménager un appartement indépendant.", "Habitation saine et très bon rapport qualité prix", "héritage", "HLM ( budget )", "IDEALEMENT PLACE PAR RAPPORT AUX COMMODITES ET A NOS LIEUX DE TRAVAIL RESPECTIFS", "Il convient à mes attentes sur le plan de la propreté", "il me plaisait et a un jardin", "Il nous plaisait", "ils nous plaisaient", "indépendance", "J’aime la montagne", "JE SAVAIS PAS QUE J AVAIS LA MUCO A L EPOQUE", "JE SUIS HEBERGER A DROITE ET A GAUHE", "La campagne", "la maison nous plaisait", "La situation par rapport au travail et au cadre de vie", "lieu", "lieu de travail parental", "Lieu selon famille et emplois", "Logement acheté par mes parents lors de mon arrivée à LYON", "Logement CROUS", "Logement de mon conjoint", "logement en ville, proche du lieu de travail", "Logement occupé pendant une recherche immobilière et des travaux de rénovation dans la maison dont nous sommes propriétaires", "logement proche du lieu de travail de mon mari", "logement sain et neuf", "Maison construite à notre convenance", "Maison familliale", "manque de moyens financiers", "Me rapprocher du travail et diminuer ma fatigue", "mes études", "Mise à disposition gratuitement par mes parents.", "mon logement n’est pas adapté aux soins", "mon père est le gestionnaire locatif", "Ne pas être trop loin de mes parents", "par envie", "Par envie", "Par goût sans lien avec ma maladie", "par goût!", "Par manque de moyens financiers", "par plaisir et par commodité professionnelle", "par rapport à mon lieu de travail", "par rapport à mon lieu de travail et celui de mon conjoint", "Parce qu’il était pas loin de la maison où je vivais avant. Il n’est pas cher.", "Parce qu’il me plaisait", "parce qu’il me plaisait et qu’il est en centre-ville", "parce qu’il nous plaisait", "PARCE QU’IL NOUS PLAISAIT", "Parce que j’avais un emploi proposé", "parce que la maison et l’endroit me plaisaient", "Parcqu?il me plaisait & qu?il était en centre ville", "pas de choix en HLM", "pas pour une raison médicale!", "Placement financier", "pour avoir enfin un logement à nous et dans nos prix", "pour avoir un logement décent", "pour devenir proprétaire", "Pour être à la campagne", "Pour être avec mes parents", "Pour être près de mon travail", "pour etre proche du bouleau", "Pour être proches de notre travail", "pour l’environnement et à proximité des grands-parents", "pour le quartier", "Pour les études et travail", "Pour me rapproche de ma soeur", "Pour me rapprocher de la fac", "Pour me rapprocher de ma famille", "pour me rapprocher de ma mère et de ma soeur", "Pour me rapprocher de mon copain", "pour me rapprocher de mon lieu de travail", "pour mes études", "Pour mes études", "pour ne pas changer de kiné", "Pour qu’il soit proche de mon lieu de travail et de mon cabinet de kiné", "Pour se rapprocher du travail et des transports en commun", "POUR UNE QUALITE DE VIE", "Pour une raison de proximité avec le lien ou j’étudie", "pour vivre chez ma conjointe", "PRES DE L ECOLE", "près de ma famille", "pres du travaille", "proche de l’école", "proche de mon boulot et école de mon fils", "Proche de mon école", "proche de mon lieu de travail", "Proche de mon lieu de travail", "PROCHE DE MON LYCEE", "proche de mon travail", "PROCHE DE MON TRAVAIL", "Proche des commerces et du travail, rapport surface et prix", "Proche des transports", "proche du college", "proche du kiné respiratoire", "Proche du lieu de travail", "proche du lieu de travail, de ma famille, mes amis", "Proche du travail", "PROCHE DU TRAVAIL", "proche du travail et quartier sympa", "proche école et plein centre ville", "Proche lieu de travail", "proprete pas de moisissure,récent,situation géographique", "proximité avec le lieu de travail", "Proximité avec le lieu de travail", "proximite du lieu de travail", "Proximité famille", "proximité lieu d’étude", "proximité lieu de travail", "proximité lycée BTS", "Proximité lycée. Logement à mon gout.", "PROXIMITE TRAVAIL", "proximité travail", "Proximité Université, CRCM, Kiné", "pvrcequ’il me plait", "Qualité et confort de vie", "Raison autres que médicales", "raison financière", "Raison professionnelle", "RAPPRCHEMENT LIEU DE TRAVAIL", "Rapprochement du lieu de travail de mon conjoint", "rapprochement familiale", "rapprochement famliale", "ras", "Retrouver mes kinés compétents et me rapprocher de ma maman", "rien à voir avec la muco", "SE RAPPROCHER DE MON ECOLE", "selon nos exigences personnelles indépendantes de la maladie", "Situation géographique pour le travail et environnement", "Souhait d’un plus grand logement", "Succession", "toujours vécu ici, maison familliale", "travail", "Travail et proches", "Un 2 pièces le plus proche de Paris", "un enfant en tant que maman solo né 2012", "vivre avec mon conjoint".
lo_occup_logement
| Variable type |
integer |
| Number of missing obs. |
91 (20.04 %) |
| Number of unique values |
6 |
| Median |
3 |
| 1st and 3rd quartiles |
1; 5 |
| Min. and max. |
1; 999 |
lo_occup_logement_autre
| Variable type |
character |
| Number of missing obs. |
441 (97.14 %) |
| Number of unique values |
13 |
| Mode |
“Co propriétaire” |
- Observed factor levels: "Co propriétaire", "Comcubinage", "EN ATTENTE LOGEMENT HLM", "Hébergé par mon conjoint qui est propriétaire", "HÉBERGÉE PAR MON CONJOINT", "Je règle une indemnité à ma mère propriétaire", "Je vis actuellement chez mes parents", "Logé à titre gratuit chez mon conjoint propriétair", "logé par mon employeur", "Logement appartenant à mon conjoint", "LOGEMENT DE FONCTION", "Loyer gratuit contre service", "Mon compagnon est propriétaire".
lo_situ_dep
| Variable type |
integer |
| Number of missing obs. |
124 (27.31 %) |
| Number of unique values |
72 |
| Median |
59 |
| 1st and 3rd quartiles |
31; 71 |
| Min. and max. |
1; 974 |
lo_situ_logement
| Variable type |
integer |
| Number of missing obs. |
92 (20.26 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
lo_situ_pays
| Variable type |
integer |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
3 |
| Median |
133.5 |
| 1st and 3rd quartiles |
131; 203.25 |
| Min. and max. |
131; 405 |
lo_type_logement
| Variable type |
integer |
| Number of missing obs. |
91 (20.04 %) |
| Number of unique values |
5 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 3 |
| Min. and max. |
1; 999 |
lo_type_logement_autre
| Variable type |
character |
| Number of missing obs. |
450 (99.12 %) |
| Number of unique values |
4 |
| Mode |
“Chambre dans une maison individuelle” |
- Observed factor levels: "Chambre dans une maison individuelle", "Chez mes parents", "Maison mitoyenne", "PLUS DE LOGEMENT".
lo_type
- The variable only takes one (non-missing) value: "P". The variable contains 19.82 % missing observations.
id_lo_cat
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
3 |
| Mode |
“id_01_lo_05” |
- Observed factor levels: "id_01", "id_01_lo_05", "lo_05".
lo_dep_centre1
| Variable type |
integer |
| Number of missing obs. |
40 (8.81 %) |
| Number of unique values |
29 |
| Median |
67 |
| 1st and 3rd quartiles |
37; 75 |
| Min. and max. |
6; 974 |
lo_dep_situ_centre1
| Variable type |
character |
| Number of missing obs. |
40 (8.81 %) |
| Number of unique values |
2 |
| Mode |
“Départements différents” |
- Observed factor levels: "Départements différents", "Même département".
qv_ad_act02
- The variable only takes one (non-missing) value: "1". The variable contains 98.68 % missing observations.
qv_ad_act03
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
qv_ad_act04
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
qv_ad_act05
- The variable only takes one (non-missing) value: "1". The variable contains 96.7 % missing observations.
qv_ad_act06
- The variable only takes one (non-missing) value: "1". The variable contains 88.77 % missing observations.
qv_ad_act07
- The variable only takes one (non-missing) value: "1". The variable contains 99.78 % missing observations.
qv_ad_act08
- The variable only takes one (non-missing) value: "1". The variable contains 95.81 % missing observations.
qv_ad_act09
- The variable only takes one (non-missing) value: "1". The variable contains 92.29 % missing observations.
qv_ad_act10
- The variable only takes one (non-missing) value: "1". The variable contains 98.46 % missing observations.
qv_ad_act01
- The variable only takes one (non-missing) value: "1". The variable contains 96.92 % missing observations.
qv_ad_act999
- The variable only takes one (non-missing) value: "1". The variable contains 99.56 % missing observations.
qv_ad_act_autre
- The variable only takes one (non-missing) value: "Assistant social, Référent Insertion professionnel". The variable contains 99.78 % missing observations.
qv_ad_besoin
| Variable type |
integer |
| Number of missing obs. |
99 (21.81 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_ad_lien
| Variable type |
integer |
| Number of missing obs. |
415 (91.41 %) |
| Number of unique values |
4 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 6 |
qv_ad_type
| Variable type |
integer |
| Number of missing obs. |
400 (88.11 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1.75 |
| Min. and max. |
1; 3 |
qv_am_ald
| Variable type |
integer |
| Number of missing obs. |
103 (22.69 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
qv_am_complement
| Variable type |
integer |
| Number of missing obs. |
106 (23.35 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
qv_am_rac
| Variable type |
integer |
| Number of missing obs. |
116 (25.55 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
qv_am_rac_montant
| Variable type |
integer |
| Number of missing obs. |
350 (77.09 %) |
| Number of unique values |
24 |
| Median |
30 |
| 1st and 3rd quartiles |
15; 70 |
| Min. and max. |
-9; 300 |
qv_am_rac_pour_1
- The variable only takes one (non-missing) value: "1". The variable contains 77.09 % missing observations.
qv_am_rac_pour_2
- The variable only takes one (non-missing) value: "1". The variable contains 91.85 % missing observations.
qv_am_rac_pour_3
- The variable only takes one (non-missing) value: "1". The variable contains 94.05 % missing observations.
qv_am_rac_pour_4
- The variable only takes one (non-missing) value: "1". The variable contains 96.04 % missing observations.
qv_am_rac_pour_5
- The variable only takes one (non-missing) value: "1". The variable contains 97.58 % missing observations.
qv_am_rac_pour_6
- The variable only takes one (non-missing) value: "1". The variable contains 96.26 % missing observations.
qv_am_rac_pour_7
- The variable only takes one (non-missing) value: "1". The variable contains 91.41 % missing observations.
qv_am_rac_pour_8
- The variable only takes one (non-missing) value: "1". The variable contains 89.65 % missing observations.
qv_am_rac_pour_9
- The variable only takes one (non-missing) value: "1". The variable contains 92.95 % missing observations.
qv_am_rac_pour_999
- The variable only takes one (non-missing) value: "1". The variable contains 96.7 % missing observations.
qv_am_rac_pour_autre
| Variable type |
character |
| Number of missing obs. |
439 (96.7 %) |
| Number of unique values |
15 |
| Mode |
“acupuncture, ostéopathie” |
- Observed factor levels: "acupuncture, ostéopathie", "Appareils Auditives", "BOUTEILLES D EAU", "Chriropratie, vitamines…", "dentiste", "déplacement médicale", "Frais de déplacement avec véhicule personnel", "hépar (eau)", "Médicaments prescrits, non remboursés, nécessaires", "osteopathe", "ostéophate", "pile et produits nettoyants pour appareils auditif", "soins : ostéopathe - naturopathe", "soins parrallèles ostéo kinésiologue", "Vitamines, micro-kinésithérapie,ostéopathe,voyage".
qv_am_regime
| Variable type |
integer |
| Number of missing obs. |
103 (22.69 %) |
| Number of unique values |
8 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 4 |
| Min. and max. |
1; 999 |
qv_am_regime_autre
| Variable type |
character |
| Number of missing obs. |
435 (95.81 %) |
| Number of unique values |
19 |
| Mode |
“caisse de prevoyance sncf” |
- Observed factor levels: "caisse de prevoyance sncf", "Caisse de prevoyance sncf", "Caisse des Français à l’Etranger &régime irlandais", "CAISSE MONEGASQUE", "CAMIEG", "CMU Frontalier", "Cpam", "CPAM", "CPR", "EDF Camiege", "MGEN", "Régime des IEG", "Regime local", "Régime local (Alsace-Moselle)", "Régime local Alsace Moselle", "régime local Alsace-Moselle", "Régime minier", "Sécurité sociale", "SNCF".
qv_am_type_1
- The variable only takes one (non-missing) value: "1". The variable contains 33.92 % missing observations.
qv_am_type_2
- The variable only takes one (non-missing) value: "1". The variable contains 97.8 % missing observations.
qv_am_type_999
- The variable only takes one (non-missing) value: "1". The variable contains 98.02 % missing observations.
qv_am_type_autre
| Variable type |
character |
| Number of missing obs. |
446 (98.24 %) |
| Number of unique values |
8 |
| Mode |
“assurance perte de salaire” |
- Observed factor levels: "assurance perte de salaire", "banque", "Banque", "cmu", "CMU", "MACSF", "Mutuelle des IEG", "sécurité sociale".
qv_date_creation
| Variable type |
character |
| Number of missing obs. |
94 (20.7 %) |
| Number of unique values |
184 |
| Mode |
“2017-11-23” |
- Observed factor levels: "2017-04-01", "2017-04-07", "2017-04-10", "2017-04-11", "2017-04-12", "2017-04-13", "2017-04-19", "2017-04-20", "2017-04-22", "2017-04-25", "2017-04-26", "2017-04-27", "2017-04-28", "2017-05-03", "2017-05-04", "2017-05-06", "2017-05-07", "2017-05-08", "2017-05-09", "2017-05-10", "2017-05-11", "2017-05-12", "2017-05-16", "2017-05-17", "2017-05-18", "2017-05-19", "2017-05-20", "2017-05-24", "2017-06-01", "2017-06-02", "2017-06-15", "2017-06-19", "2017-06-20", "2017-06-21", "2017-06-22", "2017-06-23", "2017-06-24", "2017-06-27", "2017-06-28", "2017-06-29", "2017-06-30", "2017-07-01", "2017-07-02", "2017-07-04", "2017-07-05", "2017-07-06", "2017-07-07", "2017-07-11", "2017-07-12", "2017-07-18", "2017-07-20", "2017-07-22", "2017-07-24", "2017-07-25", "2017-07-27", "2017-08-01", "2017-08-03", "2017-08-08", "2017-08-14", "2017-08-17", "2017-08-20", "2017-08-30", "2017-09-06", "2017-09-07", "2017-09-20", "2017-09-21", "2017-09-25", "2017-09-27", "2017-10-05", "2017-10-26", "2017-10-29", "2017-11-02", "2017-11-13", "2017-11-18", "2017-11-20", "2017-11-21", "2017-11-23", "2017-11-24", "2017-11-26", "2017-11-27", "2017-11-28", "2017-11-30", "2017-12-04", "2017-12-07", "2017-12-11", "2017-12-12", "2017-12-13", "2017-12-15", "2017-12-19", "2017-12-21", "2017-12-22", "2017-12-27", "2017-12-28", "2018-01-02", "2018-01-04", "2018-01-14", "2018-01-23", "2018-01-27", "2018-01-30", "2018-01-31", "2018-02-01", "2018-02-04", "2018-02-08", "2018-02-11", "2018-02-12", "2018-02-14", "2018-02-16", "2018-02-20", "2018-02-25", "2018-02-26", "2018-03-09", "2018-03-11", "2018-03-12", "2018-03-28", "2018-04-16", "2018-04-29", "2018-05-03", "2018-05-04", "2018-05-13", "2018-05-16", "2018-05-31", "2018-06-26", "2018-07-26", "2018-07-30", "2018-08-09", "2018-09-04", "2018-11-22", "2018-11-23", "2018-11-24", "2018-11-26", "2018-11-27", "2018-11-28", "2018-11-30", "2018-12-02", "2018-12-03", "2018-12-04", "2018-12-06", "2018-12-10", "2018-12-11", "2018-12-12", "2018-12-13", "2018-12-15", "2018-12-16", "2018-12-17", "2018-12-21", "2018-12-24", "2018-12-26", "2018-12-27", "2018-12-28", "2018-12-29", "2018-12-30", "2018-12-31", "2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04", "2019-01-05", "2019-01-06", "2019-01-08", "2019-01-09", "2019-01-10", "2019-01-13", "2019-01-14", "2019-01-15", "2019-01-16", "2019-01-17", "2019-01-18", "2019-01-19", "2019-01-21", "2019-01-22", "2019-01-23", "2019-01-25", "2019-01-27", "2019-01-29", "2019-01-30", "2019-02-01", "2019-02-05", "2019-02-14", "2019-02-17", "2019-02-18", "2019-02-26", "2019-02-27", "2019-03-12", "2019-03-30".
qv_sa_activites
| Variable type |
integer |
| Number of missing obs. |
96 (21.15 %) |
| Number of unique values |
3 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 3 |
qv_sa_antibio
| Variable type |
integer |
| Number of missing obs. |
100 (22.03 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_sa_anxiete
| Variable type |
integer |
| Number of missing obs. |
98 (21.59 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
1; 2 |
| Min. and max. |
1; 3 |
qv_sa_autonomie
| Variable type |
integer |
| Number of missing obs. |
95 (20.93 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
qv_sa_compl
| Variable type |
integer |
| Number of missing obs. |
109 (24.01 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_sa_douleurs
| Variable type |
integer |
| Number of missing obs. |
97 (21.37 %) |
| Number of unique values |
3 |
| Median |
2 |
| 1st and 3rd quartiles |
2; 2 |
| Min. and max. |
1; 3 |
qv_sa_etat
| Variable type |
integer |
| Number of missing obs. |
121 (26.65 %) |
| Number of unique values |
7 |
| Median |
3 |
| 1st and 3rd quartiles |
2; 3 |
| Min. and max. |
1; 99 |
qv_sa_hospi
| Variable type |
integer |
| Number of missing obs. |
108 (23.79 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_sa_kine
| Variable type |
integer |
| Number of missing obs. |
103 (22.69 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
qv_sa_medic
| Variable type |
integer |
| Number of missing obs. |
96 (21.15 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_sa_mobilite
| Variable type |
integer |
| Number of missing obs. |
97 (21.37 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
1; 2 |
qv_sa_transp
| Variable type |
integer |
| Number of missing obs. |
140 (30.84 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_sc_aide_materielle
| Variable type |
integer |
| Number of missing obs. |
103 (22.69 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
qv_sc_difficultes
| Variable type |
integer |
| Number of missing obs. |
108 (23.79 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
qv_sc_heberger
| Variable type |
integer |
| Number of missing obs. |
100 (22.03 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
qv_sc_spectacle
| Variable type |
integer |
| Number of missing obs. |
97 (21.37 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
0; 1 |
| Min. and max. |
0; 1 |
qv_sc_sport
| Variable type |
integer |
| Number of missing obs. |
99 (21.81 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
qv_sc_travailleur
| Variable type |
integer |
| Number of missing obs. |
104 (22.91 %) |
| Number of unique values |
2 |
| Median |
0 |
| 1st and 3rd quartiles |
0; 0 |
| Min. and max. |
0; 1 |
qv_sc_vacances
| Variable type |
integer |
| Number of missing obs. |
98 (21.59 %) |
| Number of unique values |
2 |
| Median |
1 |
| 1st and 3rd quartiles |
1; 1 |
| Min. and max. |
0; 1 |
qv_type
- The variable only takes one (non-missing) value: "P". The variable contains 20.7 % missing observations.
id_qv_cat
| Variable type |
character |
| Number of missing obs. |
0 (0 %) |
| Number of unique values |
3 |
| Mode |
“id_01_qv_06” |
- Observed factor levels: "id_01", "id_01_qv_06", "qv_06".
Report generation information:
Created by: Alice Thomassin (username: alicethomassin).
Report creation time: Tue Jul 22 2025 14:59:18
Report was run from directory: /Users/alicethomassin/THOA_MF
dataMaid v1.4.2 [Pkg: 2025-04-13 from CRAN (R 4.5.0)]
R version 4.5.1 (2025-06-13).
Platform: aarch64-apple-darwin20(Europe/Paris).
Function call: dataMaid::makeDataReport(data = df, mode = c("summarize", "visualize", "check"), smartNum = FALSE, file = "codebook_df.Rmd", checks = list( character = "showAllFactorLevels", factor = "showAllFactorLevels", labelled = "showAllFactorLevels", haven_labelled = "showAllFactorLevels", numeric = NULL, integer = NULL, logical = NULL, Date = NULL), listChecks = FALSE, maxProbVals = Inf, codebook = TRUE, reportTitle = "Codebook for df")